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Author Information
Karol Hausman (Google Brain)
Kefan Dong (Tsinghua University)
Ken Goldberg (UC Berkeley)
Lihong Li (Google Brain)
Lin Yang (UCLA)
Lingxiao Wang (Northwestern University)
Lior Shani (Technion)
Liwei Wang (Peking University)
Loren Amdahl-Culleton (Stanford University)
Lucas Cassano (EPFL)
Marc Dymetman (NAVER Labs Europe)
Marc Bellemare (Google Brain)
Marcin Tomczak (University of Cambridge)
Margarita Castro (University of Toronto)
Marius Kloft (TU Kaiserslautern)
Marius-Constantin Dinu (LIT AI Lab / University Linz)
Markus Holzleitner (LIT AI Lab / University Linz)
Martha White (University of Alberta)
Mengdi Wang (Princeton University)
Mengdi Wang is interested in data-driven stochastic optimization and applications in machine and reinforcement learning. She received her PhD in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in 2013. At MIT, Mengdi was affiliated with the Laboratory for Information and Decision Systems and was advised by Dimitri P. Bertsekas. Mengdi became an assistant professor at Princeton in 2014. She received the Young Researcher Prize in Continuous Optimization of the Mathematical Optimization Society in 2016 (awarded once every three years).
Michael Jordan (UC Berkeley)
Mihailo Jovanovic (University of Southern California)
Ming Yu (The University of Chicago, Booth School of Business)
Minshuo Chen (Georgia Tech)
Moonkyung Ryu (Google)
Muhammad Zaheer (University of Alberta)
Naman Agarwal (Google)
Nan Jiang (University of Illinois at Urbana-Champaign)
Niao He (UIUC)
Nikolaus Yasui (University of Alberta)
Nikos Karampatziakis (Microsoft)
Nino Vieillard (Google Brain)
Ofir Nachum (Google)
Olivier Pietquin (Google Research Brain Team)
Ozan Sener (Intel Labs)
Pan Xu (University of California, Los Angeles)
Parameswaran Kamalaruban (EPFL)
Paul Mineiro (Microsoft)
Paul Rolland (EPFL)
Philip Amortila (McGill University)
Pierre-Luc Bacon (Stanford University)
Prakash Panangaden (McGill University, Montreal)
Qi Cai (Northwestern University)
Qiang Liu (UT Austin)
Quanquan Gu (UCLA)
Raihan Seraj (McGill)
Richard Sutton
Rick Valenzano (Element AI)
Robert Dadashi (Google Brain)
Rodrigo Toro Icarte (University of Toronto and Vector Institute)
I am a Ph.D. student in the knowledge representation group at the University of Toronto. I am also a member of the Canadian Artificial Intelligence Association and the Vector Institute. My supervisor is Sheila McIlraith. I did my undergrad in Computer Engineering and MSc in Computer Science at Pontificia Universidad Catolica de Chile (PUC). My master's degree was co-supervised by Alvaro Soto and Jorge Baier. While I was at PUC, I taught the undergraduate course "Introduction to Computer Programming Languages."
Roshan Shariff (University of Alberta)
Roy Fox (UC Irvine)

[Roy Fox](royf.org) is an Assistant Professor and director of the Intelligent Dynamics Lab at the Department of Computer Science at UCI. His research interests include theory and applications of reinforcement learning, algorithmic game theory, information theory, and robotics. His current research focuses on structure, exploration, and optimization in deep reinforcement learning and imitation learning of virtual and physical agents and multi-agent systems. He was previously a postdoc at UC Berkeley, where he developed algorithms and systems that interact with humans to learn structured control policies for robotics and program synthesis.
Ruosong Wang (Carnegie Mellon University)
Saeed Ghadimi (Princeton University)
Samuel Sokota (University of Alberta)
Sean Sinclair (Cornell University)
I am a second year PhD student in Operations Research and Information Engineering at Cornell University. I completed a BSc in Mathematics and Computer Science at McGill University where I worked on a project with Tony Humphries. Before returning to graduate school I spent two and a half years teaching mathematics, science, and English in a small community in rural Ghana with the Peace Corps, and after worked at National Life as a financial analyst. In general, I am interested in machine learning, statistics, and differential equations. My current work is on the theoretical underpinnings of reinforcement learning (RL) in metric spaces. These are natural models for systems involving real-time sequential decision making over continuous spaces. To facilitate RL's use on memory-constrained devices there are many challenges. The first is learning an "optimal" discretization - trading off memory requirements and algorithmic performance. The second is learning the metric when it is not clear what metric the problem is optimal to learn in. This balances the two fundamental requirements of implementable RL - approximating the optimal policy and statistical complexity for the number of samples required to learn the near optimal policy.
Sepp Hochreiter (LIT AI Lab / University Linz / IARAI)
Sergey Levine (UC Berkeley)

Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as applications in other decision-making domains. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more
Sergio Valcarcel Macua (PROWLER.io)
Sham Kakade (University of Washington)
Shangtong Zhang (University of Oxford)
Sheila McIlraith (University of Toronto)
Shie Mannor (Technion)
Shimon Whiteson (University of Oxford)
Shuai Li (Shanghai Jiao Tong University)
Shuang Qiu (University of Michigan)
Wai Lok Li (DeepMind)
Siddhartha Banerjee (Cornell University)
Sitao Luan (McGill University, Mila)
I’m a second year Ph.D. student working with Professor Doina Precup and Professor Xiao-Wen Chang on the cross area of reinforcement learning and matrix computations. I’m currently interested in approximate dynamic programming and Krylov subspace methods. I'm currently working on constructiong basis functions for value function approximation in model-based reinforcement learning.
Tamer Basar (University of Illinois at Urbana-Champaign)
Thinh Doan (University of Illinois )
Tianhe Yu (Stanford University)
Tianyi Liu (Georgia Institute of Technolodgy)
Tom Zahavy (The Technion)
Toryn Klassen (University of Toronto)
Tuo Zhao (Gatech)
Vicenç Gómez (Universitat Pompeu Fabra)
Vincent Liu (University of Alberta)
Volkan Cevher (EPFL)
Wesley Suttle (Stony Brook University)
Xiao-Wen Chang (McGill University)
Xiaohan Wei (University of Southern California)
Xiaotong Liu (Peking Uinversity)
Xingguo Li (Princeton University)
Xinyi Chen (Princeton University)
Xingyou Song (Google Brain)
Yao Liu (Stanford University)
YiDing Jiang (Google Research)
Yihao Feng (UT Austin)
I am a Ph.D student at UT Austin, where I work on Reinforcement Learning and Approximate Inference. I am looking for internships for summer 2020! Please feel free to contact me (yihao AT cs.utexas.edu) if you have open positions!
Yilun Du (MIT)
Yinlam Chow (Google Research)
Yinyu Ye (Standord)
Yishay Mansour (Tel Aviv University / Google)
Yonathan Efroni (Technion)
Yongxin Chen (Georgia Institute of Technology)
Yuanhao Wang (Tsinghua University)
Bo Dai (The Chinese University of Hong Kong)
Chen-Yu Wei (University of Southern California)
Harsh Shrivastava (Georgia Institute of Technology)
Hongyang Zhang (University of Pennsylvania)
Qinqing Zheng (University of Pennsylvania)
SIDDHARTHA SATPATHI (University of Illinois at Urbana Champaign)
I am a 4th year PhD student at ECE, UIUC working on problems in machine learning.
Xueqing Liu (UIUC)
Andreu Vall (LIT AI Lab / University Linz)
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2022 : Toward Semantic History Compression for Reinforcement Learning »
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2022 : Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts »
Amrith Setlur · Don Dennis · Benjamin Eysenbach · Aditi Raghunathan · Chelsea Finn · Virginia Smith · Sergey Levine -
2022 : Train Offline, Test Online: A Real Robot Learning Benchmark »
Gaoyue Zhou · Victoria Dean · Mohan Kumar Srirama · Aravind Rajeswaran · Jyothish Pari · Kyle Hatch · Aryan Jain · Tianhe Yu · Pieter Abbeel · Lerrel Pinto · Chelsea Finn · Abhinav Gupta -
2022 : Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information »
Riashat Islam · Manan Tomar · Alex Lamb · Hongyu Zang · Yonathan Efroni · Dipendra Misra · Aniket Didolkar · Xin Li · Harm Van Seijen · Remi Tachet des Combes · John Langford -
2022 : Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks »
Jesse Farebrother · Joshua Greaves · Rishabh Agarwal · Charline Le Lan · Ross Goroshin · Pablo Samuel Castro · Marc Bellemare -
2022 : Confidence-Conditioned Value Functions for Offline Reinforcement Learning »
Joey Hong · Aviral Kumar · Sergey Levine -
2022 : Efficient Deep Reinforcement Learning Requires Regulating Statistical Overfitting »
Qiyang Li · Aviral Kumar · Ilya Kostrikov · Sergey Levine -
2022 : Contrastive Example-Based Control »
Kyle Hatch · Sarthak J Shetty · Benjamin Eysenbach · Tianhe Yu · Rafael Rafailov · Russ Salakhutdinov · Sergey Levine · Chelsea Finn -
2022 : Trajectory-based Explainability Framework for Offline RL »
Shripad Deshmukh · Arpan Dasgupta · Chirag Agarwal · Nan Jiang · Balaji Krishnamurthy · Georgios Theocharous · Jayakumar Subramanian -
2022 : AMORE: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data »
Tengyang Xie · Mohak Bhardwaj · Nan Jiang · Ching-An Cheng -
2022 : Train Offline, Test Online: A Real Robot Learning Benchmark »
Gaoyue Zhou · Victoria Dean · Mohan Kumar Srirama · Aravind Rajeswaran · Jyothish Pari · Kyle Hatch · Aryan Jain · Tianhe Yu · Pieter Abbeel · Lerrel Pinto · Chelsea Finn · Abhinav Gupta -
2022 : Offline Reinforcement Learning for Customizable Visual Navigation »
Dhruv Shah · Arjun Bhorkar · Hrishit Leen · Ilya Kostrikov · Nicholas Rhinehart · Sergey Levine -
2022 : Provable Benefits of Representational Transfer in Reinforcement Learning »
Alekh Agarwal · Yuda Song · Kaiwen Wang · Mengdi Wang · Wen Sun · Xuezhou Zhang -
2022 : A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning »
Benjamin Eysenbach · Matthieu Geist · Sergey Levine · Russ Salakhutdinov -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 : Towards Data-Driven Offline Simulations for Online Reinforcement Learning »
Shengpu Tang · Felipe Vieira Frujeri · Dipendra Misra · Alex Lamb · John Langford · Paul Mineiro · Sebastian Kochman -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 : Fast Sampling of Diffusion Models with Exponential Integrator »
Qinsheng Zhang · Yongxin Chen -
2022 : Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint »
Hao Liu · Minshuo Chen · Siawpeng Er · Wenjing Liao · Tong Zhang · Tuo Zhao -
2022 : Valid Inference after Causal Discovery »
Paula Gradu · Tijana Zrnic · Yixin Wang · Michael Jordan -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 : Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs »
Benjamin Fuhrer · Yuval Shpigelman · Chen Tessler · Shie Mannor · Gal Chechik · Eitan Zahavi · Gal Dalal -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 : Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks »
Sitao Luan · Mingde Zhao · Chenqing Hua · Xiao-Wen Chang · Doina Precup -
2022 : Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines »
Andrew Li · Zizhao Chen · Pashootan Vaezipoor · Toryn Klassen · Rodrigo Toro Icarte · Sheila McIlraith -
2022 : Informative rewards and generalization in curriculum learning »
Rahul Siripurapu · Vihang Patil · Kajetan Schweighofer · Marius-Constantin Dinu · Markus Holzleitner · Hamid Eghbalzadeh · Luis Ferro · Thomas Schmied · Michael Kopp · Sepp Hochreiter -
2022 : Train Offline, Test Online: A Real Robot Learning Benchmark »
Gaoyue Zhou · Victoria Dean · Mohan Kumar Srirama · Aravind Rajeswaran · Jyothish Pari · Kyle Hatch · Aryan Jain · Tianhe Yu · Pieter Abbeel · Lerrel Pinto · Chelsea Finn · Abhinav Gupta -
2022 : Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments »
JB Lanier · Stephen McAleer · Pierre Baldi · Roy Fox -
2022 : Confidence-Conditioned Value Functions for Offline Reinforcement Learning »
Joey Hong · Aviral Kumar · Sergey Levine -
2022 : Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks »
Jesse Farebrother · Joshua Greaves · Rishabh Agarwal · Charline Le Lan · Ross Goroshin · Pablo Samuel Castro · Marc Bellemare -
2022 : Return Augmentation gives Supervised RL Temporal Compositionality »
Keiran Paster · Silviu Pitis · Sheila McIlraith · Jimmy Ba -
2022 : Efficient Deep Reinforcement Learning Requires Regulating Statistical Overfitting »
Qiyang Li · Aviral Kumar · Ilya Kostrikov · Sergey Levine -
2022 : Pre-Training for Robots: Leveraging Diverse Multitask Data via Offline Reinforcement Learning »
Anikait Singh · Aviral Kumar · Frederik Ebert · Yanlai Yang · Chelsea Finn · Sergey Levine -
2022 : Offline Reinforcement Learning from Heteroskedastic Data Via Support Constraints »
Anikait Singh · Aviral Kumar · Quan Vuong · Yevgen Chebotar · Sergey Levine -
2022 : Variance Double-Down: The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning »
Johan Obando Ceron · Marc Bellemare · Pablo Samuel Castro -
2022 : Foundation Models for History Compression in Reinforcement Learning »
Fabian Paischer · Thomas Adler · Andreas Radler · Markus Hofmarcher · Sepp Hochreiter -
2022 : AsymQ: Asymmetric Q-loss to mitigate overestimation bias in off-policy reinforcement learning »
Qinsheng Zhang · Arjun Krishna · Sehoon Ha · Yongxin Chen -
2022 : Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier »
Pierluca D'Oro · Max Schwarzer · Evgenii Nikishin · Pierre-Luc Bacon · Marc Bellemare · Aaron Courville -
2022 : Adversarial Policies Beat Professional-Level Go AIs »
Tony Wang · Adam Gleave · Nora Belrose · Tom Tseng · Michael Dennis · Yawen Duan · Viktor Pogrebniak · Joseph Miller · Sergey Levine · Stuart J Russell -
2022 : Contrastive Example-Based Control »
Kyle Hatch · Sarthak J Shetty · Benjamin Eysenbach · Tianhe Yu · Rafael Rafailov · Russ Salakhutdinov · Sergey Levine · Chelsea Finn -
2022 : PnP-Nav: Plug-and-Play Policies for Generalizable Visual Navigation Across Robots »
Dhruv Shah · Ajay Sridhar · Arjun Bhorkar · Noriaki Hirose · Sergey Levine -
2022 : Offline Reinforcement Learning for Customizable Visual Navigation »
Dhruv Shah · Arjun Bhorkar · Hrishit Leen · Ilya Kostrikov · Nicholas Rhinehart · Sergey Levine -
2022 : Investigating Multi-task Pretraining and Generalization in Reinforcement Learning »
Adrien Ali Taiga · Rishabh Agarwal · Jesse Farebrother · Aaron Courville · Marc Bellemare -
2022 : Contrastive Value Learning: Implicit Models for Simple Offline RL »
Bogdan Mazoure · Benjamin Eysenbach · Ofir Nachum · Jonathan Tompson -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 : SoftTreeMax: Policy Gradient with Tree Search »
Gal Dalal · Assaf Hallak · Shie Mannor · Gal Chechik -
2022 : A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning »
Benjamin Eysenbach · Matthieu Geist · Russ Salakhutdinov · Sergey Levine -
2022 : A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning »
Zixiang Chen · Chris Junchi Li · Angela Yuan · Quanquan Gu · Michael Jordan -
2022 : Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective »
Raj Ghugare · Homanga Bharadhwaj · Benjamin Eysenbach · Sergey Levine · Ruslan Salakhutdinov -
2022 : MOPA: a Minimalist Off-Policy Approach to Safe-RL »
Hao Sun · Ziping Xu · Zhenghao Peng · Meng Fang · Bo Dai · Bolei Zhou -
2022 : Adversarial Policies Beat Professional-Level Go AIs »
Tony Wang · Adam Gleave · Nora Belrose · Tom Tseng · Michael Dennis · Yawen Duan · Viktor Pogrebniak · Joseph Miller · Sergey Levine · Stuart Russell -
2022 : Epistemic Side Effects & Avoiding Them (Sometimes) »
Toryn Klassen · Parand Alizadeh Alamdari · Sheila McIlraith -
2023 Workshop: New Frontiers of AI for Drug Discovery and Development »
Animashree Anandkumar · Ilija Bogunovic · Ti-chiun Chang · Quanquan Gu · Jure Leskovec · Michelle Li · Chong Liu · Nataša Tagasovska · Wei Wang -
2022 : Debate: Robotics for Good »
Karol Hausman · Katherine Driggs-Campbell · Luca Carlone · Sarah Dean · Matthew Johnson-Roberson · Animesh Garg -
2022 : Factor Investing with a Deep Multi-Factor Model »
Zikai Wei · Bo Dai · Dahua Lin -
2022 : Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes »
Aviral Kumar · Rishabh Agarwal · XINYANG GENG · George Tucker · Sergey Levine -
2022 : Panel: Scaling & Models (Q&A 2) »
Andy Zeng · Haoran Tang · Karol Hausman · Jackie Kay · Gabriel Barth-Maron -
2022 Workshop: Deep Reinforcement Learning Workshop »
Karol Hausman · Qi Zhang · Matthew Taylor · Martha White · Suraj Nair · Manan Tomar · Risto Vuorio · Ted Xiao · Zeyu Zheng · Manan Tomar -
2022 Spotlight: A Mean-Field Game Approach to Cloud Resource Management with Function Approximation »
Weichao Mao · Haoran Qiu · Chen Wang · Hubertus Franke · Zbigniew Kalbarczyk · Ravishankar Iyer · Tamer Basar -
2022 : Weather4cast Introduction »
Sepp Hochreiter · David Kreil -
2022 Spotlight: Lightning Talks 4A-4 »
Yunhao Tang · LING LIANG · Thomas Chau · Daeha Kim · Junbiao Cui · Rui Lu · Lei Song · Byung Cheol Song · Andrew Zhao · Remi Munos · Łukasz Dudziak · Jiye Liang · Ke Xue · Kaidi Xu · Mark Rowland · Hongkai Wen · Xing Hu · Xiaobin Huang · Simon Du · Nicholas Lane · Chao Qian · Lei Deng · Bernardo Avila Pires · Gao Huang · Will Dabney · Mohamed Abdelfattah · Yuan Xie · Marc Bellemare -
2022 Spotlight: Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret »
Jiawei Huang · Li Zhao · Tao Qin · Wei Chen · Nan Jiang · Tie-Yan Liu -
2022 Spotlight: Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime »
Difan Zou · Jingfeng Wu · Vladimir Braverman · Quanquan Gu · Sham Kakade -
2022 Spotlight: The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning »
Yunhao Tang · Remi Munos · Mark Rowland · Bernardo Avila Pires · Will Dabney · Marc Bellemare -
2022 Spotlight: Lightning Talks 4A-1 »
Jiawei Huang · Su Jia · Abdurakhmon Sadiev · Ruomin Huang · Yuanyu Wan · Denizalp Goktas · Jiechao Guan · Andrew Li · Wei-Wei Tu · Li Zhao · Amy Greenwald · Jiawei Huang · Dmitry Kovalev · Yong Liu · Wenjie Liu · Peter Richtarik · Lijun Zhang · Zhiwu Lu · R Ravi · Tao Qin · Wei Chen · Hu Ding · Nan Jiang · Tie-Yan Liu -
2022 Spotlight: Lightning Talks 3B-3 »
Sitao Luan · Zhiyuan You · Ruofan Liu · Linhao Qu · Yuwei Fu · Jiaxi Wang · Chunyu Wei · Jian Liang · xiaoyuan luo · Di Wu · Yun Lin · Lei Cui · Ji Wu · Chenqing Hua · Yujun Shen · Qincheng Lu · XIANGLIN YANG · Benoit Boulet · Manning Wang · Di Liu · Lei Huang · Fei Wang · Kai Yang · Jiaqi Zhu · Jin Song Dong · Zhijian Song · Xin Lu · Mingde Zhao · Shuyuan Zhang · Yu Zheng · Xiao-Wen Chang · Xinyi Le · Doina Precup -
2022 Spotlight: Revisiting Heterophily For Graph Neural Networks »
Sitao Luan · Chenqing Hua · Qincheng Lu · Jiaqi Zhu · Mingde Zhao · Shuyuan Zhang · Xiao-Wen Chang · Doina Precup -
2022 Spotlight: Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power »
Binghui Li · Jikai Jin · Han Zhong · John Hopcroft · Liwei Wang -
2022 Spotlight: Lightning Talks 2A-1 »
Caio Kalil Lauand · Ryan Strauss · Yasong Feng · lingyu gu · Alireza Fathollah Pour · Oren Mangoubi · Jianhao Ma · Binghui Li · Hassan Ashtiani · Yongqi Du · Salar Fattahi · Sean Meyn · Jikai Jin · Nisheeth Vishnoi · zengfeng Huang · Junier B Oliva · yuan zhang · Han Zhong · Tianyu Wang · John Hopcroft · Di Xie · Shiliang Pu · Liwei Wang · Robert Qiu · Zhenyu Liao -
2022 : Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs »
Benjamin Fuhrer · Yuval Shpigelman · Chen Tessler · Shie Mannor · Gal Chechik · Eitan Zahavi · Gal Dalal -
2022 : Train Offline, Test Online: A Real Robot Learning Benchmark »
Gaoyue Zhou · Victoria Dean · Mohan Kumar Srirama · Aravind Rajeswaran · Jyothish Pari · Kyle Hatch · Aryan Jain · Tianhe Yu · Pieter Abbeel · Lerrel Pinto · Chelsea Finn · Abhinav Gupta -
2022 : Train Offline, Test Online: A Real Robot Learning Benchmark »
Gaoyue Zhou · Victoria Dean · Mohan Kumar Srirama · Aravind Rajeswaran · Jyothish Pari · Kyle Hatch · Aryan Jain · Tianhe Yu · Pieter Abbeel · Lerrel Pinto · Chelsea Finn · Abhinav Gupta -
2022 : Contributed Talks 2 »
Quanquan Gu · Aaron Defazio · Jiajin Li -
2022 Workshop: OPT 2022: Optimization for Machine Learning »
Courtney Paquette · Sebastian Stich · Quanquan Gu · Cristóbal Guzmán · John Duchi -
2022 Workshop: Foundation Models for Decision Making »
Mengjiao (Sherry) Yang · Yilun Du · Jack Parker-Holder · Siddharth Karamcheti · Igor Mordatch · Shixiang (Shane) Gu · Ofir Nachum -
2022 Workshop: Reinforcement Learning for Real Life (RL4RealLife) Workshop »
Yuxi Li · Emma Brunskill · MINMIN CHEN · Omer Gottesman · Lihong Li · Yao Liu · Zhiwei Tony Qin · Matthew Taylor -
2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang -
2022 : Panel Discussion: Opportunities and Challenges »
Kenneth Norman · Janice Chen · Samuel J Gershman · Albert Gu · Sepp Hochreiter · Ida Momennejad · Hava Siegelmann · Sainbayar Sukhbaatar -
2022 : Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings. »
Ajay Chawda · Marius Kloft · Stefanie Grimm -
2022 : Mechanisms that Incentivize Data Sharing in Federated Learning »
Sai Praneeth Karimireddy · Wenshuo Guo · Michael Jordan -
2022 : Sepp Hochreiter: "Modern Hopfield Networks" »
Sepp Hochreiter -
2022 Workshop: Has it Trained Yet? A Workshop for Algorithmic Efficiency in Practical Neural Network Training »
Frank Schneider · Zachary Nado · Philipp Hennig · George Dahl · Naman Agarwal -
2022 Poster: Communication Efficient Distributed Learning for Kernelized Contextual Bandits »
Chuanhao Li · Huazheng Wang · Mengdi Wang · Hongning Wang -
2022 Poster: Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization »
Ali Kavis · Stratis Skoulakis · Kimon Antonakopoulos · Leello Tadesse Dadi · Volkan Cevher -
2022 Poster: Oracle Inequalities for Model Selection in Offline Reinforcement Learning »
Jonathan N Lee · George Tucker · Ofir Nachum · Bo Dai · Emma Brunskill -
2022 Poster: Towards Understanding the Mixture-of-Experts Layer in Deep Learning »
Zixiang Chen · Yihe Deng · Yue Wu · Quanquan Gu · Yuanzhi Li -
2022 Poster: MEMO: Test Time Robustness via Adaptation and Augmentation »
Marvin Zhang · Sergey Levine · Chelsea Finn -
2022 Poster: Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs »
Dongruo Zhou · Quanquan Gu -
2022 Poster: Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions »
Audrey Huang · Nan Jiang -
2022 Poster: Chain of Thought Imitation with Procedure Cloning »
Mengjiao (Sherry) Yang · Dale Schuurmans · Pieter Abbeel · Ofir Nachum -
2022 Poster: Riemannian Diffusion Models »
Chin-Wei Huang · Milad Aghajohari · Joey Bose · Prakash Panangaden · Aaron Courville -
2022 Poster: First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization »
Siddharth Reddy · Sergey Levine · Anca Dragan -
2022 Poster: Off-Policy Evaluation with Policy-Dependent Optimization Response »
Wenshuo Guo · Michael Jordan · Angela Zhou -
2022 Poster: No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation »
Yu-Guan Hsieh · Kimon Antonakopoulos · Volkan Cevher · Panayotis Mertikopoulos -
2022 Poster: First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces »
Michael Jordan · Tianyi Lin · Emmanouil-Vasileios Vlatakis-Gkaragkounis -
2022 Poster: Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress »
Rishabh Agarwal · Max Schwarzer · Pablo Samuel Castro · Aaron Courville · Marc Bellemare -
2022 Poster: The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning »
Yunhao Tang · Remi Munos · Mark Rowland · Bernardo Avila Pires · Will Dabney · Marc Bellemare -
2022 Poster: Revisiting Heterophily For Graph Neural Networks »
Sitao Luan · Chenqing Hua · Qincheng Lu · Jiaqi Zhu · Mingde Zhao · Shuyuan Zhang · Xiao-Wen Chang · Doina Precup -
2022 Poster: Is $L^2$ Physics Informed Loss Always Suitable for Training Physics Informed Neural Network? »
Chuwei Wang · Shanda Li · Di He · Liwei Wang -
2022 Poster: Multi-Game Decision Transformers »
Kuang-Huei Lee · Ofir Nachum · Mengjiao (Sherry) Yang · Lisa Lee · Daniel Freeman · Sergio Guadarrama · Ian Fischer · Winnie Xu · Eric Jang · Henryk Michalewski · Igor Mordatch -
2022 Poster: Learning to Follow Instructions in Text-Based Games »
Mathieu Tuli · Andrew Li · Pashootan Vaezipoor · Toryn Klassen · Scott Sanner · Sheila McIlraith -
2022 Poster: Tsetlin Machine for Solving Contextual Bandit Problems »
Raihan Seraj · Jivitesh Sharma · Ole-Christoffer Granmo -
2022 Poster: Generalization Properties of NAS under Activation and Skip Connection Search »
Zhenyu Zhu · Fanghui Liu · Grigorios Chrysos · Volkan Cevher -
2022 Poster: Benign Overfitting in Two-layer Convolutional Neural Networks »
Yuan Cao · Zixiang Chen · Misha Belkin · Quanquan Gu -
2022 Poster: Tractable Optimality in Episodic Latent MABs »
Jeongyeol Kwon · Yonathan Efroni · Constantine Caramanis · Shie Mannor -
2022 Poster: DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning »
Quan Vuong · Aviral Kumar · Sergey Levine · Yevgen Chebotar -
2022 Poster: Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power »
Binghui Li · Jikai Jin · Han Zhong · John Hopcroft · Liwei Wang -
2022 Poster: Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization) »
Zhenyu Zhu · Fanghui Liu · Grigorios Chrysos · Volkan Cevher -
2022 Poster: Emergent Communication: Generalization and Overfitting in Lewis Games »
Mathieu Rita · Corentin Tallec · Paul Michel · Jean-Bastien Grill · Olivier Pietquin · Emmanuel Dupoux · Florian Strub -
2022 Poster: On the Double Descent of Random Features Models Trained with SGD »
Fanghui Liu · Johan Suykens · Volkan Cevher -
2022 Poster: A Mean-Field Game Approach to Cloud Resource Management with Function Approximation »
Weichao Mao · Haoran Qiu · Chen Wang · Hubertus Franke · Zbigniew Kalbarczyk · Ravishankar Iyer · Tamer Basar -
2022 Poster: Your Transformer May Not be as Powerful as You Expect »
Shengjie Luo · Shanda Li · Shuxin Zheng · Tie-Yan Liu · Liwei Wang · Di He -
2022 Poster: Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning »
Paul Rolland · Luca Viano · Norman Schürhoff · Boris Nikolov · Volkan Cevher -
2022 Poster: In Defense of the Unitary Scalarization for Deep Multi-Task Learning »
Vitaly Kurin · Alessandro De Palma · Ilya Kostrikov · Shimon Whiteson · Pawan K Mudigonda -
2022 Poster: CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP »
Andreas Fürst · Elisabeth Rumetshofer · Johannes Lehner · Viet T. Tran · Fei Tang · Hubert Ramsauer · David Kreil · Michael Kopp · Günter Klambauer · Angela Bitto · Sepp Hochreiter -
2022 Poster: Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study »
Yongtao Wu · Zhenyu Zhu · Fanghui Liu · Grigorios Chrysos · Volkan Cevher -
2022 Poster: Proximal Point Imitation Learning »
Luca Viano · Angeliki Kamoutsi · Gergely Neu · Igor Krawczuk · Volkan Cevher -
2022 Poster: Interaction-Grounded Learning with Action-Inclusive Feedback »
Tengyang Xie · Akanksha Saran · Dylan J Foster · Lekan Molu · Ida Momennejad · Nan Jiang · Paul Mineiro · John Langford -
2022 Poster: Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium »
Chris Junchi Li · Dongruo Zhou · Quanquan Gu · Michael Jordan -
2022 Poster: Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets »
Yifei Min · Tianhao Wang · Ruitu Xu · Zhaoran Wang · Michael Jordan · Zhuoran Yang -
2022 Poster: A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits »
Jiafan He · Tianhao Wang · Yifei Min · Quanquan Gu -
2022 Poster: Adversarial Unlearning: Reducing Confidence Along Adversarial Directions »
Amrith Setlur · Benjamin Eysenbach · Virginia Smith · Sergey Levine -
2022 Poster: Mismatched No More: Joint Model-Policy Optimization for Model-Based RL »
Benjamin Eysenbach · Alexander Khazatsky · Sergey Levine · Russ Salakhutdinov -
2022 Poster: Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games »
Sihan Zeng · Thinh Doan · Justin Romberg -
2022 Poster: Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration »
Fanghui Liu · Luca Viano · Volkan Cevher -
2022 Poster: Robust Calibration with Multi-domain Temperature Scaling »
Yaodong Yu · Stephen Bates · Yi Ma · Michael Jordan -
2022 Poster: On-Demand Sampling: Learning Optimally from Multiple Distributions »
Nika Haghtalab · Michael Jordan · Eric Zhao -
2022 Poster: A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation »
Philip Amortila · Nan Jiang · Dhruv Madeka · Dean Foster -
2022 Poster: The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift »
Jingfeng Wu · Difan Zou · Vladimir Braverman · Quanquan Gu · Sham Kakade -
2022 Poster: Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity »
Abhishek Gupta · Aldo Pacchiano · Yuexiang Zhai · Sham Kakade · Sergey Levine -
2022 Poster: Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime »
Difan Zou · Jingfeng Wu · Vladimir Braverman · Quanquan Gu · Sham Kakade -
2022 Poster: You Can’t Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments »
Keiran Paster · Sheila McIlraith · Jimmy Ba -
2022 Poster: Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters »
Kamyar Ghasemipour · Shixiang (Shane) Gu · Ofir Nachum -
2022 Poster: On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL »
Jinglin Chen · Aditya Modi · Akshay Krishnamurthy · Nan Jiang · Alekh Agarwal -
2022 Poster: Distributionally Adaptive Meta Reinforcement Learning »
Anurag Ajay · Abhishek Gupta · Dibya Ghosh · Sergey Levine · Pulkit Agrawal -
2022 Poster: On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds »
Biraj Dahal · Alexander Havrilla · Minshuo Chen · Tuo Zhao · Wenjing Liao -
2022 Poster: Improving GANs with A Dynamic Discriminator »
Ceyuan Yang · Yujun Shen · Yinghao Xu · Deli Zhao · Bo Dai · Bolei Zhou -
2022 Poster: You Only Live Once: Single-Life Reinforcement Learning »
Annie Chen · Archit Sharma · Sergey Levine · Chelsea Finn -
2022 Poster: Towards Learning Universal Hyperparameter Optimizers with Transformers »
Yutian Chen · Xingyou Song · Chansoo Lee · Zi Wang · Richard Zhang · David Dohan · Kazuya Kawakami · Greg Kochanski · Arnaud Doucet · Marc'Aurelio Ranzato · Sagi Perel · Nando de Freitas -
2022 Poster: Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization »
Tianyi Lin · Zeyu Zheng · Michael Jordan -
2022 Poster: Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective »
Bohang Zhang · Du Jiang · Di He · Liwei Wang -
2022 Poster: Reinforcement Learning with a Terminator »
Guy Tennenholtz · Nadav Merlis · Lior Shani · Shie Mannor · Uri Shalit · Gal Chechik · Assaf Hallak · Gal Dalal -
2022 Poster: Truncated Emphatic Temporal Difference Methods for Prediction and Control »
Shangtong Zhang · Shimon Whiteson -
2022 Poster: Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation »
Michael Chang · Tom Griffiths · Sergey Levine -
2022 Poster: Finite Sample Analysis Of Dynamic Regression Parameter Learning »
Mark Kozdoba · Edward Moroshko · Shie Mannor · Yacov Crammer -
2022 Poster: Sound and Complete Verification of Polynomial Networks »
Elias Abad Rocamora · Mehmet Fatih Sahin · Fanghui Liu · Grigorios Chrysos · Volkan Cevher -
2022 Poster: Data-Driven Offline Decision-Making via Invariant Representation Learning »
Han Qi · Yi Su · Aviral Kumar · Sergey Levine -
2022 Poster: Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations »
Albert Wilcox · Ashwin Balakrishna · Jules Dedieu · Wyame Benslimane · Daniel Brown · Ken Goldberg -
2022 Poster: Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions »
Jiafan He · Dongruo Zhou · Tong Zhang · Quanquan Gu -
2022 Poster: Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods »
Kimon Antonakopoulos · Ali Kavis · Volkan Cevher -
2022 Poster: Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks »
Shuoguang Yang · Xuezhou Zhang · Mengdi Wang -
2022 Poster: Contrastive Learning as Goal-Conditioned Reinforcement Learning »
Benjamin Eysenbach · Tianjun Zhang · Sergey Levine · Russ Salakhutdinov -
2022 Poster: TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels »
Yaodong Yu · Alexander Wei · Sai Praneeth Karimireddy · Yi Ma · Michael Jordan -
2022 Poster: Empirical Gateaux Derivatives for Causal Inference »
Michael Jordan · Yixin Wang · Angela Zhou -
2022 Poster: Active Ranking without Strong Stochastic Transitivity »
Hao Lou · Tao Jin · Yue Wu · Pan Xu · Quanquan Gu · Farzad Farnoud -
2022 Poster: Continuous MDP Homomorphisms and Homomorphic Policy Gradient »
Sahand Rezaei-Shoshtari · Rosie Zhao · Prakash Panangaden · David Meger · Doina Precup -
2022 Poster: Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization »
Hui Yuan · Chengzhuo Ni · Huazheng Wang · Xuezhou Zhang · Le Cong · Csaba Szepesvari · Mengdi Wang -
2022 Poster: Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning »
Guy Tennenholtz · Shie Mannor -
2022 Poster: Imitating Past Successes can be Very Suboptimal »
Benjamin Eysenbach · Soumith Udatha · Russ Salakhutdinov · Sergey Levine -
2022 Poster: Improving Zero-Shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions »
Bogdan Mazoure · Ilya Kostrikov · Ofir Nachum · Jonathan Tompson -
2022 Poster: Provably sample-efficient RL with side information about latent dynamics »
Yao Liu · Dipendra Misra · Miro Dudik · Robert Schapire -
2022 Poster: Equivariant Networks for Zero-Shot Coordination »
Darius Muglich · Christian Schroeder de Witt · Elise van der Pol · Shimon Whiteson · Jakob Foerster -
2022 Poster: Learning Energy Networks with Generalized Fenchel-Young Losses »
Mathieu Blondel · Felipe Llinares-Lopez · Robert Dadashi · Leonard Hussenot · Matthieu Geist -
2022 Poster: Efficient Risk-Averse Reinforcement Learning »
Ido Greenberg · Yinlam Chow · Mohammad Ghavamzadeh · Shie Mannor -
2022 Poster: Myriad: a real-world testbed to bridge trajectory optimization and deep learning »
Nikolaus Howe · Simon Dufort-Labbé · Nitarshan Rajkumar · Pierre-Luc Bacon -
2021 : Retrospective Panel »
Sergey Levine · Nando de Freitas · Emma Brunskill · Finale Doshi-Velez · Nan Jiang · Rishabh Agarwal -
2021 : Invited Speaker Panel »
Sham Kakade · Minmin Chen · Philip Thomas · Angela Schoellig · Barbara Engelhardt · Doina Precup · George Tucker -
2021 : Q&A for Sham Kakade »
Sham Kakade -
2021 : Generalization theory in Offline RL »
Sham Kakade -
2021 : Reinforcement Learning in Factored Action Spaces using Tensor Decompositions »
Anuj Mahajan · Mikayel Samvelyan · Lei Mao · Viktor Makoviichuk · Animesh Garg · Jean Kossaifi · Shimon Whiteson · Yuke Zhu · Anima Anandkumar -
2021 : Model based multi-agent reinforcement learning with tensor decompositions »
Pascal van der Vaart · Anuj Mahajan · Shimon Whiteson -
2021 : Understanding the Effects of Dataset Composition on Offline Reinforcement Learning »
Kajetan Schweighofer · Markus Hofmarcher · Marius-Constantin Dinu · Angela Bitto · Philipp Renz · Vihang Patil · Sepp Hochreiter -
2021 Workshop: Offline Reinforcement Learning »
Rishabh Agarwal · Aviral Kumar · George Tucker · Justin Fu · Nan Jiang · Doina Precup · Aviral Kumar -
2021 : Understanding the Effects of Dataset Composition on Offline Reinforcement Learning »
Kajetan Schweighofer · Markus Hofmarcher · Marius-Constantin Dinu · Angela Bitto · Philipp Renz · Vihang Patil · Sepp Hochreiter -
2021 Workshop: Ecological Theory of Reinforcement Learning: How Does Task Design Influence Agent Learning? »
Manfred Díaz · Hiroki Furuta · Elise van der Pol · Lisa Lee · Shixiang (Shane) Gu · Pablo Samuel Castro · Simon Du · Marc Bellemare · Sergey Levine -
2021 Workshop: Advances in Programming Languages and Neurosymbolic Systems (AIPLANS) »
Breandan Considine · Disha Shrivastava · David Yu-Tung Hui · Chin-Wei Huang · Shawn Tan · Xujie Si · Prakash Panangaden · Guy Van den Broeck · Daniel Tarlow -
2021 : Karol Hausman Talk Q&A »
Karol Hausman -
2021 : Invited Talk: Karol Hausman - Reinforcement Learning as a Data Sponge »
Karol Hausman -
2021 : Data-Driven Offline Optimization for Architecting Hardware Accelerators »
Aviral Kumar · Amir Yazdanbakhsh · Milad Hashemi · Kevin Swersky · Sergey Levine -
2021 : Contributed talks in Session 4 (Zoom) »
Quanquan Gu · Agnieszka Słowik · Jacques Chen · Neha Wadia · Difan Zou -
2021 : Sergey Levine Talk Q&A »
Sergey Levine -
2021 : Opinion Contributed Talk: Sergey Levine »
Sergey Levine -
2021 : Online Learning via Linear Programming, Yinyu Ye »
Yinyu Ye -
2021 : Opening Remarks to Session 4 »
Quanquan Gu -
2021 : Offline Meta-Reinforcement Learning with Online Self-Supervision Q&A »
Vitchyr Pong · Ashvin Nair · Laura Smith · Catherine Huang · Sergey Levine -
2021 : Offline Meta-Reinforcement Learning with Online Self-Supervision »
Vitchyr Pong · Ashvin Nair · Laura Smith · Catherine Huang · Sergey Levine -
2021 : Offline Meta-Reinforcement Learning with Online Self-Supervision »
Vitchyr Pong · Ashvin Nair · Laura Smith · Catherine Huang · Sergey Levine -
2021 : Safe RL Panel Discussion »
Animesh Garg · Marek Petrik · Shie Mannor · Claire Tomlin · Ugo Rosolia · Dylan Hadfield-Menell -
2021 : Shie Mannor »
Shie Mannor -
2021 : DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization Q&A »
Aviral Kumar · Rishabh Agarwal · Tengyu Ma · Aaron Courville · George Tucker · Sergey Levine -
2021 : Shie Mannor »
Shie Mannor -
2021 : DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization »
Aviral Kumar · Rishabh Agarwal · Tengyu Ma · Aaron Courville · George Tucker · Sergey Levine -
2021 Workshop: Distribution shifts: connecting methods and applications (DistShift) »
Shiori Sagawa · Pang Wei Koh · Fanny Yang · Hongseok Namkoong · Jiashi Feng · Kate Saenko · Percy Liang · Sarah Bird · Sergey Levine -
2021 Workshop: Deep Reinforcement Learning »
Pieter Abbeel · Chelsea Finn · David Silver · Matthew Taylor · Martha White · Srijita Das · Yuqing Du · Andrew Patterson · Manan Tomar · Olivia Watkins -
2021 Workshop: Learning in Presence of Strategic Behavior »
Omer Ben-Porat · Nika Haghtalab · Annie Liang · Yishay Mansour · David Parkes -
2021 : Bootstrapped Meta-Learning »
Sebastian Flennerhag · Yannick Schroecker · Tom Zahavy · Hado van Hasselt · David Silver · Satinder Singh -
2021 Workshop: OPT 2021: Optimization for Machine Learning »
Courtney Paquette · Quanquan Gu · Oliver Hinder · Katya Scheinberg · Sebastian Stich · Martin Takac -
2021 Oral: Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification »
Ben Eysenbach · Sergey Levine · Russ Salakhutdinov -
2021 Poster: Accelerating Quadratic Optimization with Reinforcement Learning »
Jeffrey Ichnowski · Paras Jain · Bartolomeo Stellato · Goran Banjac · Michael Luo · Francesco Borrelli · Joseph Gonzalez · Ion Stoica · Ken Goldberg -
2021 Poster: Learning to Compose Visual Relations »
Nan Liu · Shuang Li · Yilun Du · Josh Tenenbaum · Antonio Torralba -
2021 Poster: Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning »
Siyuan Zhang · Nan Jiang -
2021 Poster: Curriculum Design for Teaching via Demonstrations: Theory and Applications »
Gaurav Yengera · Rati Devidze · Parameswaran Kamalaruban · Adish Singla -
2021 Poster: Explicable Reward Design for Reinforcement Learning Agents »
Rati Devidze · Goran Radanovic · Parameswaran Kamalaruban · Adish Singla -
2021 Poster: Visual Adversarial Imitation Learning using Variational Models »
Rafael Rafailov · Tianhe Yu · Aravind Rajeswaran · Chelsea Finn -
2021 Poster: The Effect of the Intrinsic Dimension on the Generalization of Quadratic Classifiers »
Fabian Latorre · Leello Tadesse Dadi · Paul Rolland · Volkan Cevher -
2021 Poster: Convergence of adaptive algorithms for constrained weakly convex optimization »
Ahmet Alacaoglu · Yura Malitsky · Volkan Cevher -
2021 Poster: Bellman-consistent Pessimism for Offline Reinforcement Learning »
Tengyang Xie · Ching-An Cheng · Nan Jiang · Paul Mineiro · Alekh Agarwal -
2021 Poster: Minimax Regret for Stochastic Shortest Path »
Alon Cohen · Yonathan Efroni · Yishay Mansour · Aviv Rosenberg -
2021 Poster: Reward is enough for convex MDPs »
Tom Zahavy · Brendan O'Donoghue · Guillaume Desjardins · Satinder Singh -
2021 Poster: The Benefits of Implicit Regularization from SGD in Least Squares Problems »
Difan Zou · Jingfeng Wu · Vladimir Braverman · Quanquan Gu · Dean Foster · Sham Kakade -
2021 Poster: Robust Learning of Optimal Auctions »
Wenshuo Guo · Michael Jordan · Emmanouil Zampetakis -
2021 Poster: Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL »
Minshuo Chen · Yan Li · Ethan Wang · Zhuoran Yang · Zhaoran Wang · Tuo Zhao -
2021 Poster: Robust and differentially private mean estimation »
Xiyang Liu · Weihao Kong · Sham Kakade · Sewoong Oh -
2021 Poster: A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis »
Xingang Pan · Xudong XU · Chen Change Loy · Christian Theobalt · Bo Dai -
2021 Poster: Learning Signal-Agnostic Manifolds of Neural Fields »
Yilun Du · Katie Collins · Josh Tenenbaum · Vincent Sitzmann -
2021 Poster: Robust Predictable Control »
Ben Eysenbach · Russ Salakhutdinov · Sergey Levine -
2021 Poster: Efficiently Identifying Task Groupings for Multi-Task Learning »
Chris Fifty · Ehsan Amid · Zhe Zhao · Tianhe Yu · Rohan Anil · Chelsea Finn -
2021 Poster: Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation »
Jiafan He · Dongruo Zhou · Quanquan Gu -
2021 Poster: Which Mutual-Information Representation Learning Objectives are Sufficient for Control? »
Kate Rakelly · Abhishek Gupta · Carlos Florensa · Sergey Levine -
2021 Poster: Learning in Multi-Stage Decentralized Matching Markets »
Xiaowu Dai · Michael Jordan -
2021 Poster: An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap »
Yuanhao Wang · Ruosong Wang · Sham Kakade -
2021 Poster: Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent »
Spencer Frei · Quanquan Gu -
2021 Poster: COMBO: Conservative Offline Model-Based Policy Optimization »
Tianhe Yu · Aviral Kumar · Rafael Rafailov · Aravind Rajeswaran · Sergey Levine · Chelsea Finn -
2021 : Traffic4cast 2021 – Temporal and Spatial Few-Shot Transfer Learning in Traffic Map Movie Forecasting + Q&A »
Moritz Neun · Christian Eichenberger · Henry Martin · Pedro Herruzo · David Jonietz · Fei Tang · Daniel Springer · Markus Spanring · Avi Avidan · Luis Ferro · Ali Soleymani · Rohit Gupta · Bo Xu · Kevin Malm · Aleksandra Gruca · Johannes Brandstetter · Michael Kopp · David Kreil · Sepp Hochreiter -
2021 Poster: FACMAC: Factored Multi-Agent Centralised Policy Gradients »
Bei Peng · Tabish Rashid · Christian Schroeder de Witt · Pierre-Alexandre Kamienny · Philip Torr · Wendelin Boehmer · Shimon Whiteson -
2021 Poster: Outcome-Driven Reinforcement Learning via Variational Inference »
Tim G. J. Rudner · Vitchyr Pong · Rowan McAllister · Yarin Gal · Sergey Levine -
2021 Poster: Bayesian Bellman Operators »
Mattie Fellows · Kristian Hartikainen · Shimon Whiteson -
2021 Poster: Who Leads and Who Follows in Strategic Classification? »
Tijana Zrnic · Eric Mazumdar · Shankar Sastry · Michael Jordan -
2021 Poster: Unsupervised Learning of Compositional Energy Concepts »
Yilun Du · Shuang Li · Yash Sharma · Josh Tenenbaum · Igor Mordatch -
2021 Poster: Safe Reinforcement Learning with Natural Language Constraints »
Tsung-Yen Yang · Michael Y Hu · Yinlam Chow · Peter J. Ramadge · Karthik Narasimhan -
2021 Poster: Twice regularized MDPs and the equivalence between robustness and regularization »
Esther Derman · Matthieu Geist · Shie Mannor -
2021 Poster: Test-time Collective Prediction »
Celestine Mendler-Dünner · Wenshuo Guo · Stephen Bates · Michael Jordan -
2021 Poster: Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures »
Yuan Cao · Quanquan Gu · Mikhail Belkin -
2021 Poster: On the Theory of Reinforcement Learning with Once-per-Episode Feedback »
Niladri Chatterji · Aldo Pacchiano · Peter Bartlett · Michael Jordan -
2021 Poster: RL for Latent MDPs: Regret Guarantees and a Lower Bound »
Jeongyeol Kwon · Yonathan Efroni · Constantine Caramanis · Shie Mannor -
2021 Poster: Understanding Bandits with Graph Feedback »
Houshuang Chen · zengfeng Huang · Shuai Li · Chihao Zhang -
2021 Poster: Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs »
Jiafan He · Dongruo Zhou · Quanquan Gu -
2021 Poster: Going Beyond Linear RL: Sample Efficient Neural Function Approximation »
Baihe Huang · Kaixuan Huang · Sham Kakade · Jason Lee · Qi Lei · Runzhe Wang · Jiaqi Yang -
2021 Poster: Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis »
Jikai Jin · Bohang Zhang · Haiyang Wang · Liwei Wang -
2021 Poster: BooVI: Provably Efficient Bootstrapped Value Iteration »
Boyi Liu · Qi Cai · Zhuoran Yang · Zhaoran Wang -
2021 Poster: Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic »
Yufeng Zhang · Siyu Chen · Zhuoran Yang · Michael Jordan · Zhaoran Wang -
2021 Poster: STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization »
Kfir Levy · Ali Kavis · Volkan Cevher -
2021 Poster: Decentralized Q-learning in Zero-sum Markov Games »
Muhammed Sayin · Kaiqing Zhang · David Leslie · Tamer Basar · Asuman Ozdaglar -
2021 Poster: There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning »
Nathan Grinsztajn · Johan Ferret · Olivier Pietquin · philippe preux · Matthieu Geist -
2021 Poster: LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes »
Aditya Kusupati · Matthew Wallingford · Vivek Ramanujan · Raghav Somani · Jae Sung Park · Krishna Pillutla · Prateek Jain · Sham Kakade · Ali Farhadi -
2021 Poster: Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs »
Han Zhong · Jiayi Huang · Lin Yang · Liwei Wang -
2021 Poster: Subquadratic Overparameterization for Shallow Neural Networks »
ChaeHwan Song · Ali Ramezani-Kebrya · Thomas Pethick · Armin Eftekhari · Volkan Cevher -
2021 Poster: Tactical Optimism and Pessimism for Deep Reinforcement Learning »
Ted Moskovitz · Jack Parker-Holder · Aldo Pacchiano · Michael Arbel · Michael Jordan -
2021 Poster: Sim and Real: Better Together »
Shirli Di-Castro · Dotan Di Castro · Shie Mannor -
2021 Poster: Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation »
Weitong ZHANG · Dongruo Zhou · Quanquan Gu -
2021 Poster: Bayesian Adaptation for Covariate Shift »
Aurick Zhou · Sergey Levine -
2021 Poster: Gone Fishing: Neural Active Learning with Fisher Embeddings »
Jordan Ash · Surbhi Goel · Akshay Krishnamurthy · Sham Kakade -
2021 Poster: Offline Reinforcement Learning as One Big Sequence Modeling Problem »
Michael Janner · Qiyang Li · Sergey Levine -
2021 Poster: Variance-Aware Off-Policy Evaluation with Linear Function Approximation »
Yifei Min · Tianhao Wang · Dongruo Zhou · Quanquan Gu -
2021 Poster: Pragmatic Image Compression for Human-in-the-Loop Decision-Making »
Sid Reddy · Anca Dragan · Sergey Levine -
2021 Poster: A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning »
Mingde Zhao · Zhen Liu · Sitao Luan · Shuyuan Zhang · Doina Precup · Yoshua Bengio -
2021 Poster: Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity »
Kaiqing Zhang · Xiangyuan Zhang · Bin Hu · Tamer Basar -
2021 Poster: On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method »
Junyu Zhang · Chengzhuo Ni · zheng Yu · Csaba Szepesvari · Mengdi Wang -
2021 Poster: Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification »
Ben Eysenbach · Sergey Levine · Russ Salakhutdinov -
2021 Oral: Deep Reinforcement Learning at the Edge of the Statistical Precipice »
Rishabh Agarwal · Max Schwarzer · Pablo Samuel Castro · Aaron Courville · Marc Bellemare -
2021 Oral: Bellman-consistent Pessimism for Offline Reinforcement Learning »
Tengyang Xie · Ching-An Cheng · Nan Jiang · Paul Mineiro · Alekh Agarwal -
2021 Poster: The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle »
Fang Kong · Yueran Yang · Wei Chen · Shuai Li -
2021 Poster: Sifting through the noise: Universal first-order methods for stochastic variational inequalities »
Kimon Antonakopoulos · Thomas Pethick · Ali Kavis · Panayotis Mertikopoulos · Volkan Cevher -
2021 Poster: Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding »
Shengjie Luo · Shanda Li · Tianle Cai · Di He · Dinglan Peng · Shuxin Zheng · Guolin Ke · Liwei Wang · Tie-Yan Liu -
2021 Poster: Generative Occupancy Fields for 3D Surface-Aware Image Synthesis »
Xudong XU · Xingang Pan · Dahua Lin · Bo Dai -
2021 Poster: Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch »
Luca Viano · Yu-Ting Huang · Parameswaran Kamalaruban · Adrian Weller · Volkan Cevher -
2021 Poster: Regularized Softmax Deep Multi-Agent Q-Learning »
Ling Pan · Tabish Rashid · Bei Peng · Longbo Huang · Shimon Whiteson -
2021 Poster: Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction »
Gal Dalal · Assaf Hallak · Steven Dalton · iuri frosio · Shie Mannor · Gal Chechik -
2021 Poster: What Matters for Adversarial Imitation Learning? »
Manu Orsini · Anton Raichuk · Leonard Hussenot · Damien Vincent · Robert Dadashi · Sertan Girgin · Matthieu Geist · Olivier Bachem · Olivier Pietquin · Marcin Andrychowicz -
2021 Poster: Iterative Teacher-Aware Learning »
Luyao Yuan · Dongruo Zhou · Junhong Shen · Jingdong Gao · Jeffrey L Chen · Quanquan Gu · Ying Nian Wu · Song-Chun Zhu -
2021 Poster: Information is Power: Intrinsic Control via Information Capture »
Nicholas Rhinehart · Jenny Wang · Glen Berseth · John Co-Reyes · Danijar Hafner · Chelsea Finn · Sergey Levine -
2021 Poster: Conservative Data Sharing for Multi-Task Offline Reinforcement Learning »
Tianhe Yu · Aviral Kumar · Yevgen Chebotar · Karol Hausman · Sergey Levine · Chelsea Finn -
2021 Poster: XDO: A Double Oracle Algorithm for Extensive-Form Games »
Stephen McAleer · JB Lanier · Kevin A Wang · Pierre Baldi · Roy Fox -
2021 Poster: Fine-grained Generalization Analysis of Inductive Matrix Completion »
Antoine Ledent · Rodrigo Alves · Yunwen Lei · Marius Kloft -
2021 Poster: Diffusion Normalizing Flow »
Qinsheng Zhang · Yongxin Chen -
2021 Poster: Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data »
Liming Jiang · Bo Dai · Wayne Wu · Chen Change Loy -
2021 Poster: Discovery of Options via Meta-Learned Subgoals »
Vivek Veeriah · Tom Zahavy · Matteo Hessel · Zhongwen Xu · Junhyuk Oh · Iurii Kemaev · Hado van Hasselt · David Silver · Satinder Singh -
2021 Poster: Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses »
Haipeng Luo · Chen-Yu Wei · Chung-Wei Lee -
2021 Poster: Towards a Theoretical Framework of Out-of-Distribution Generalization »
Haotian Ye · Chuanlong Xie · Tianle Cai · Ruichen Li · Zhenguo Li · Liwei Wang -
2021 Poster: Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints »
Tianhao Wang · Dongruo Zhou · Quanquan Gu -
2021 Poster: Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks »
Hanxun Huang · Yisen Wang · Sarah Erfani · Quanquan Gu · James Bailey · Xingjun Ma -
2021 Poster: Learning Equilibria in Matching Markets from Bandit Feedback »
Meena Jagadeesan · Alexander Wei · Yixin Wang · Michael Jordan · Jacob Steinhardt -
2021 Poster: Do Wider Neural Networks Really Help Adversarial Robustness? »
Boxi Wu · Jinghui Chen · Deng Cai · Xiaofei He · Quanquan Gu -
2021 Poster: Deep Reinforcement Learning at the Edge of the Statistical Precipice »
Rishabh Agarwal · Max Schwarzer · Pablo Samuel Castro · Aaron Courville · Marc Bellemare -
2021 Poster: Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning »
Tengyang Xie · Nan Jiang · Huan Wang · Caiming Xiong · Yu Bai -
2021 Poster: Pure Exploration in Kernel and Neural Bandits »
Yinglun Zhu · Dongruo Zhou · Ruoxi Jiang · Quanquan Gu · Rebecca Willett · Robert Nowak -
2021 Poster: A first-order primal-dual method with adaptivity to local smoothness »
Maria-Luiza Vladarean · Yura Malitsky · Volkan Cevher -
2021 Poster: Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability »
Dibya Ghosh · Jad Rahme · Aviral Kumar · Amy Zhang · Ryan Adams · Sergey Levine -
2021 Poster: Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing »
Charles Blake · Vitaly Kurin · Maximilian Igl · Shimon Whiteson -
2021 Poster: On Component Interactions in Two-Stage Recommender Systems »
Jiri Hron · Karl Krauth · Michael Jordan · Niki Kilbertus -
2021 Poster: Design of Experiments for Stochastic Contextual Linear Bandits »
Andrea Zanette · Kefan Dong · Jonathan N Lee · Emma Brunskill -
2021 Poster: Optimal Gradient-based Algorithms for Non-concave Bandit Optimization »
Baihe Huang · Kaixuan Huang · Sham Kakade · Jason Lee · Qi Lei · Runzhe Wang · Jiaqi Yang -
2021 Poster: The balancing principle for parameter choice in distance-regularized domain adaptation »
Werner Zellinger · Natalia Shepeleva · Marius-Constantin Dinu · Hamid Eghbal-zadeh · Hoan Duc Nguyen · Bernhard Nessler · Sergei Pereverzyev · Bernhard A. Moser -
2021 Poster: Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning »
Hanzhe Hu · Fangyun Wei · Han Hu · Qiwei Ye · Jinshi Cui · Liwei Wang -
2021 Poster: Monte Carlo Tree Search With Iteratively Refining State Abstractions »
Samuel Sokota · Caleb Y Ho · Zaheen Ahmad · J. Zico Kolter -
2021 Poster: MICo: Improved representations via sampling-based state similarity for Markov decision processes »
Pablo Samuel Castro · Tyler Kastner · Prakash Panangaden · Mark Rowland -
2021 Poster: Autonomous Reinforcement Learning via Subgoal Curricula »
Archit Sharma · Abhishek Gupta · Sergey Levine · Karol Hausman · Chelsea Finn -
2021 Poster: Reinforcement Learning in Reward-Mixing MDPs »
Jeongyeol Kwon · Yonathan Efroni · Constantine Caramanis · Shie Mannor -
2021 Poster: Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature »
Kefan Dong · Jiaqi Yang · Tengyu Ma -
2021 Poster: Dynamic Bottleneck for Robust Self-Supervised Exploration »
Chenjia Bai · Lingxiao Wang · Lei Han · Animesh Garg · Jianye Hao · Peng Liu · Zhaoran Wang -
2021 Poster: Adaptive Risk Minimization: Learning to Adapt to Domain Shift »
Marvin Zhang · Henrik Marklund · Nikita Dhawan · Abhishek Gupta · Sergey Levine · Chelsea Finn -
2021 Poster: Provably Efficient Causal Reinforcement Learning with Confounded Observational Data »
Lingxiao Wang · Zhuoran Yang · Zhaoran Wang -
2021 Oral: An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap »
Yuanhao Wang · Ruosong Wang · Sham Kakade -
2020 : Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization »
Brandon Trabucco · Aviral Kumar · XINYANG GENG · Sergey Levine -
2020 : Conservative Objective Models: A Simple Approach to Effective Model-Based Optimization »
Brandon Trabucco · Aviral Kumar · XINYANG GENG · Sergey Levine -
2020 : Contributed Talk 6: What are the Statistical Limits for Batch RL with Linear Function Approximation? »
Ruosong Wang -
2020 : Contributed Talk 6: Do Offline Metrics Predict Online Performance in Recommender Systems? »
Karl Krauth · Sarah Dean · Wenshuo Guo · Benjamin Recht · Michael Jordan -
2020 : Towards Reliable Validation and Evaluation for Offline RL »
Nan Jiang -
2020 : Contributed Talk #3: Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning »
Rishabh Agarwal · Marlos C. Machado · Pablo Samuel Castro · Marc Bellemare -
2020 : Panel »
Emma Brunskill · Nan Jiang · Nando de Freitas · Finale Doshi-Velez · Sergey Levine · John Langford · Lihong Li · George Tucker · Rishabh Agarwal · Aviral Kumar -
2020 : Panel discussion »
Pierre-Yves Oudeyer · Marc Bellemare · Peter Stone · Matt Botvinick · Susan Murphy · Anusha Nagabandi · Ashley Edwards · Karen Liu · Pieter Abbeel -
2020 : Closing remarks »
Quanquan Gu · Courtney Paquette · Mark Schmidt · Sebastian Stich · Martin Takac -
2020 : Traffic Map Movies - An Introduction to the Traffic4cast Challenge »
Sepp Hochreiter -
2020 : Contributed talks in Session 4 (Zoom) »
Quanquan Gu · sanae lotfi · Charles Guille-Escuret · Tolga Ergen · Dongruo Zhou -
2020 : Live Q&A with Deanna Needell and Hanbake Lyu (Zoom) »
Quanquan Gu -
2020 : Welcome remarks to Session 4 »
Quanquan Gu -
2020 : Contributed Talk: MaxEnt RL and Robust Control »
Benjamin Eysenbach · Sergey Levine -
2020 : Contributed Talk: Planning from Pixels using Inverse Dynamics Models »
Keiran Paster · Sheila McIlraith · Jimmy Ba -
2020 : Contributed Talk: Mirror Descent Policy Optimization »
Manan Tomar · Lior Shani · Yonathan Efroni · Mohammad Ghavamzadeh -
2020 : Invited talk: Marc Bellemare "Autonomous navigation of stratospheric balloons using reinforcement learning" »
Marc Bellemare -
2020 : Invited speaker: Adaptation and universality in first-order methods, Volkan Cevher »
Volkan Cevher -
2020 Workshop: OPT2020: Optimization for Machine Learning »
Courtney Paquette · Mark Schmidt · Sebastian Stich · Quanquan Gu · Martin Takac -
2020 : Welcome event (gather.town) »
Quanquan Gu · Courtney Paquette · Mark Schmidt · Sebastian Stich · Martin Takac -
2020 Poster: Projection Robust Wasserstein Distance and Riemannian Optimization »
Tianyi Lin · Chenyou Fan · Nhat Ho · Marco Cuturi · Michael Jordan -
2020 Poster: Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm »
Tianyi Lin · Nhat Ho · Xi Chen · Marco Cuturi · Michael Jordan -
2020 Poster: Escaping Saddle-Point Faster under Interpolation-like Conditions »
Abhishek Roy · Krishnakumar Balasubramanian · Saeed Ghadimi · Prasant Mohapatra -
2020 Poster: Simple and Fast Algorithm for Binary Integer and Online Linear Programming »
Xiaocheng Li · Chunlin Sun · Yinyu Ye -
2020 Poster: Model Inversion Networks for Model-Based Optimization »
Aviral Kumar · Sergey Levine -
2020 Poster: Generalized Leverage Score Sampling for Neural Networks »
Jason Lee · Ruoqi Shen · Zhao Song · Mengdi Wang · zheng Yu -
2020 Poster: A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks »
Zixiang Chen · Yuan Cao · Quanquan Gu · Tong Zhang -
2020 Poster: Improved Analysis of Clipping Algorithms for Non-convex Optimization »
Bohang Zhang · Jikai Jin · Cong Fang · Liwei Wang -
2020 Spotlight: Projection Robust Wasserstein Distance and Riemannian Optimization »
Tianyi Lin · Chenyou Fan · Nhat Ho · Marco Cuturi · Michael Jordan -
2020 Session: Orals & Spotlights Track 34: Deep Learning »
Tuo Zhao · Jimmy Ba -
2020 Tutorial: (Track3) Policy Optimization in Reinforcement Learning Q&A »
Sham M Kakade · Martha White · Nicolas Le Roux -
2020 Poster: Decision-Making with Auto-Encoding Variational Bayes »
Romain Lopez · Pierre Boyeau · Nir Yosef · Michael Jordan · Jeffrey Regier -
2020 Poster: Sample Complexity of Uniform Convergence for Multicalibration »
Eliran Shabat · Lee Cohen · Yishay Mansour -
2020 Poster: Sharper Generalization Bounds for Pairwise Learning »
Yunwen Lei · Antoine Ledent · Marius Kloft -
2020 Poster: High-Dimensional Sparse Linear Bandits »
Botao Hao · Tor Lattimore · Mengdi Wang -
2020 Poster: Munchausen Reinforcement Learning »
Nino Vieillard · Olivier Pietquin · Matthieu Geist -
2020 Poster: Empirical Likelihood for Contextual Bandits »
Nikos Karampatziakis · John Langford · Paul Mineiro -
2020 Poster: Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning »
Nino Vieillard · Tadashi Kozuno · Bruno Scherrer · Olivier Pietquin · Remi Munos · Matthieu Geist -
2020 Poster: Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices »
John Duchi · Oliver Hinder · Andrew Naber · Yinyu Ye -
2020 Poster: Continual Learning of Control Primitives : Skill Discovery via Reset-Games »
Kelvin Xu · Siddharth Verma · Chelsea Finn · Sergey Levine -
2020 Poster: Stein Self-Repulsive Dynamics: Benefits From Past Samples »
Mao Ye · Tongzheng Ren · Qiang Liu -
2020 Oral: Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning »
Nino Vieillard · Tadashi Kozuno · Bruno Scherrer · Olivier Pietquin · Remi Munos · Matthieu Geist -
2020 Poster: Gradient Surgery for Multi-Task Learning »
Tianhe Yu · Saurabh Kumar · Abhishek Gupta · Sergey Levine · Karol Hausman · Chelsea Finn -
2020 Poster: Transferable Calibration with Lower Bias and Variance in Domain Adaptation »
Ximei Wang · Mingsheng Long · Jianmin Wang · Michael Jordan -
2020 Poster: Robust Meta-learning for Mixed Linear Regression with Small Batches »
Weihao Kong · Raghav Somani · Sham Kakade · Sewoong Oh -
2020 Poster: Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs »
Chung-Wei Lee · Haipeng Luo · Chen-Yu Wei · Mengxiao Zhang -
2020 Oral: Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs »
Chung-Wei Lee · Haipeng Luo · Chen-Yu Wei · Mengxiao Zhang -
2020 Poster: Modern Hopfield Networks and Attention for Immune Repertoire Classification »
Michael Widrich · Bernhard Schäfl · Milena Pavlović · Hubert Ramsauer · Lukas Gruber · Markus Holzleitner · Johannes Brandstetter · Geir Kjetil Sandve · Victor Greiff · Sepp Hochreiter · Günter Klambauer -
2020 Poster: An implicit function learning approach for parametric modal regression »
Yangchen Pan · Ehsan Imani · Amir-massoud Farahmand · Martha White -
2020 Poster: Prediction with Corrupted Expert Advice »
Idan Amir · Idan Attias · Tomer Koren · Yishay Mansour · Roi Livni -
2020 Poster: Towards Safe Policy Improvement for Non-Stationary MDPs »
Yash Chandak · Scott Jordan · Georgios Theocharous · Martha White · Philip Thomas -
2020 Poster: Differentiable Top-k with Optimal Transport »
Yujia Xie · Hanjun Dai · Minshuo Chen · Bo Dai · Tuo Zhao · Hongyuan Zha · Wei Wei · Tomas Pfister -
2020 Poster: Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective »
Kaixuan Huang · Yuqing Wang · Molei Tao · Tuo Zhao -
2020 Poster: On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems »
Panayotis Mertikopoulos · Nadav Hallak · Ali Kavis · Volkan Cevher -
2020 Poster: Stochastic Optimization with Laggard Data Pipelines »
Naman Agarwal · Rohan Anil · Tomer Koren · Kunal Talwar · Cyril Zhang -
2020 Spotlight: Towards Safe Policy Improvement for Non-Stationary MDPs »
Yash Chandak · Scott Jordan · Georgios Theocharous · Martha White · Philip Thomas -
2020 Spotlight: Prediction with Corrupted Expert Advice »
Idan Amir · Idan Attias · Tomer Koren · Yishay Mansour · Roi Livni -
2020 Spotlight: Modern Hopfield Networks and Attention for Immune Repertoire Classification »
Michael Widrich · Bernhard Schäfl · Milena Pavlović · Hubert Ramsauer · Lukas Gruber · Markus Holzleitner · Johannes Brandstetter · Geir Kjetil Sandve · Victor Greiff · Sepp Hochreiter · Günter Klambauer -
2020 Poster: Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement »
Benjamin Eysenbach · XINYANG GENG · Sergey Levine · Russ Salakhutdinov -
2020 Poster: Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework »
Dinghuai Zhang · Mao Ye · Chengyue Gong · Zhanxing Zhu · Qiang Liu -
2020 Poster: Robust Optimization for Fairness with Noisy Protected Groups »
Serena Wang · Wenshuo Guo · Harikrishna Narasimhan · Andrew Cotter · Maya Gupta · Michael Jordan -
2020 Poster: Certified Monotonic Neural Networks »
Xingchao Liu · Xing Han · Na Zhang · Qiang Liu -
2020 Poster: Locally Differentially Private (Contextual) Bandits Learning »
Kai Zheng · Tianle Cai · Weiran Huang · Zhenguo Li · Liwei Wang -
2020 Poster: Online Influence Maximization under Linear Threshold Model »
Shuai Li · Fang Kong · Kejie Tang · Qizhi Li · Wei Chen -
2020 Poster: Planning with General Objective Functions: Going Beyond Total Rewards »
Ruosong Wang · Peilin Zhong · Simon Du · Russ Salakhutdinov · Lin Yang -
2020 Poster: Is Long Horizon RL More Difficult Than Short Horizon RL? »
Ruosong Wang · Simon Du · Lin Yang · Sham Kakade -
2020 Poster: Conservative Q-Learning for Offline Reinforcement Learning »
Aviral Kumar · Aurick Zhou · George Tucker · Sergey Levine -
2020 Poster: Compositional Visual Generation with Energy Based Models »
Yilun Du · Shuang Li · Igor Mordatch -
2020 Poster: Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning »
Tabish Rashid · Gregory Farquhar · Bei Peng · Shimon Whiteson -
2020 Poster: Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory »
Yufeng Zhang · Qi Cai · Zhuoran Yang · Yongxin Chen · Zhaoran Wang -
2020 Poster: Preference-based Reinforcement Learning with Finite-Time Guarantees »
Yichong Xu · Ruosong Wang · Lin Yang · Aarti Singh · Artur Dubrawski -
2020 Poster: Latent Bandits Revisited »
Joey Hong · Branislav Kveton · Manzil Zaheer · Yinlam Chow · Amr Ahmed · Craig Boutilier -
2020 Spotlight: Preference-based Reinforcement Learning with Finite-Time Guarantees »
Yichong Xu · Ruosong Wang · Lin Yang · Aarti Singh · Artur Dubrawski -
2020 Spotlight: Certified Monotonic Neural Networks »
Xingchao Liu · Xing Han · Na Zhang · Qiang Liu -
2020 Spotlight: Compositional Visual Generation with Energy Based Models »
Yilun Du · Shuang Li · Igor Mordatch -
2020 Oral: Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory »
Yufeng Zhang · Qi Cai · Zhuoran Yang · Yongxin Chen · Zhaoran Wang -
2020 Oral: Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement »
Benjamin Eysenbach · XINYANG GENG · Sergey Levine · Russ Salakhutdinov -
2020 Session: Orals & Spotlights Track 14: Reinforcement Learning »
Deepak Pathak · Martha White -
2020 Tutorial: (Track3) Offline Reinforcement Learning: From Algorithm Design to Practical Applications Q&A »
Sergey Levine · Aviral Kumar -
2020 Poster: Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot »
Jingtong Su · Yihang Chen · Tianle Cai · Tianhao Wu · Ruiqi Gao · Liwei Wang · Jason Lee -
2020 Poster: Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver? »
Vitaly Kurin · Saad Godil · Shimon Whiteson · Bryan Catanzaro -
2020 Poster: Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity »
Simon Du · Jason Lee · Gaurav Mahajan · Ruosong Wang -
2020 Poster: Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction »
Michael Janner · Igor Mordatch · Sergey Levine -
2020 Poster: Adversarially Robust Streaming Algorithms via Differential Privacy »
Avinatan Hassidim · Haim Kaplan · Yishay Mansour · Yossi Matias · Uri Stemmer -
2020 Poster: Learning Retrospective Knowledge with Reverse Reinforcement Learning »
Shangtong Zhang · Vivek Veeriah · Shimon Whiteson -
2020 Poster: An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods »
Yanli Liu · Kaiqing Zhang · Tamer Basar · Wotao Yin -
2020 Poster: One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL »
Saurabh Kumar · Aviral Kumar · Sergey Levine · Chelsea Finn -
2020 Poster: RepPoints v2: Verification Meets Regression for Object Detection »
Yihong Chen · Zheng Zhang · Yue Cao · Liwei Wang · Stephen Lin · Han Hu -
2020 Poster: Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity »
Haim Kaplan · Yishay Mansour · Uri Stemmer · Eliad Tsfadia -
2020 Poster: A Self-Tuning Actor-Critic Algorithm »
Tom Zahavy · Zhongwen Xu · Vivek Veeriah · Matteo Hessel · Junhyuk Oh · Hado van Hasselt · David Silver · Satinder Singh -
2020 Poster: Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks »
Lemeng Wu · Bo Liu · Peter Stone · Qiang Liu -
2020 Poster: Distributionally Robust Local Non-parametric Conditional Estimation »
Viet Anh Nguyen · Fan Zhang · Jose Blanchet · Erick Delage · Yinyu Ye -
2020 Poster: Variational Policy Gradient Method for Reinforcement Learning with General Utilities »
Junyu Zhang · Alec Koppel · Amrit Singh Bedi · Csaba Szepesvari · Mengdi Wang -
2020 Poster: POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis »
Weichao Mao · Kaiqing Zhang · Qiaomin Xie · Tamer Basar -
2020 Poster: Agnostic Learning of a Single Neuron with Gradient Descent »
Spencer Frei · Yuan Cao · Quanquan Gu -
2020 Poster: On the Theory of Transfer Learning: The Importance of Task Diversity »
Nilesh Tripuraneni · Michael Jordan · Chi Jin -
2020 Poster: FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs »
Alekh Agarwal · Sham Kakade · Akshay Krishnamurthy · Wen Sun -
2020 Poster: On Reward-Free Reinforcement Learning with Linear Function Approximation »
Ruosong Wang · Simon Du · Lin Yang · Russ Salakhutdinov -
2020 Poster: Towards Understanding Hierarchical Learning: Benefits of Neural Representations »
Minshuo Chen · Yu Bai · Jason Lee · Tuo Zhao · Huan Wang · Caiming Xiong · Richard Socher -
2020 Poster: Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough »
Mao Ye · Lemeng Wu · Qiang Liu -
2020 Poster: PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning »
Alekh Agarwal · Mikael Henaff · Sham Kakade · Wen Sun -
2020 Poster: Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors »
Karl Pertsch · Oleh Rybkin · Frederik Ebert · Shenghao Zhou · Dinesh Jayaraman · Chelsea Finn · Sergey Levine -
2020 Poster: Online Planning with Lookahead Policies »
Yonathan Efroni · Mohammad Ghavamzadeh · Shie Mannor -
2020 Poster: Robust Reinforcement Learning via Adversarial training with Langevin Dynamics »
Parameswaran Kamalaruban · Yu-Ting Huang · Ya-Ping Hsieh · Paul Rolland · Cheng Shi · Volkan Cevher -
2020 Poster: Sample-Efficient Reinforcement Learning of Undercomplete POMDPs »
Chi Jin · Sham Kakade · Akshay Krishnamurthy · Qinghua Liu -
2020 Poster: Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications »
Sarah Perrin · Julien Perolat · Mathieu Lauriere · Matthieu Geist · Romuald Elie · Olivier Pietquin -
2020 Poster: Escaping the Gravitational Pull of Softmax »
Jincheng Mei · Chenjun Xiao · Bo Dai · Lihong Li · Csaba Szepesvari · Dale Schuurmans -
2020 Poster: Adaptive Discretization for Model-Based Reinforcement Learning »
Sean Sinclair · Tianyu Wang · Gauri Jain · Siddhartha Banerjee · Christina Yu -
2020 Poster: Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model »
Alex X. Lee · Anusha Nagabandi · Pieter Abbeel · Sergey Levine -
2020 Poster: Efficient Planning in Large MDPs with Weak Linear Function Approximation »
Roshan Shariff · Csaba Szepesvari -
2020 Spotlight: Sample-Efficient Reinforcement Learning of Undercomplete POMDPs »
Chi Jin · Sham Kakade · Akshay Krishnamurthy · Qinghua Liu -
2020 Spotlight: Variational Policy Gradient Method for Reinforcement Learning with General Utilities »
Junyu Zhang · Alec Koppel · Amrit Singh Bedi · Csaba Szepesvari · Mengdi Wang -
2020 Oral: FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs »
Alekh Agarwal · Sham Kakade · Akshay Krishnamurthy · Wen Sun -
2020 Oral: Escaping the Gravitational Pull of Softmax »
Jincheng Mei · Chenjun Xiao · Bo Dai · Lihong Li · Csaba Szepesvari · Dale Schuurmans -
2020 Oral: Adversarially Robust Streaming Algorithms via Differential Privacy »
Avinatan Hassidim · Haim Kaplan · Yishay Mansour · Yossi Matias · Uri Stemmer -
2020 Poster: Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning »
Fei Feng · Ruosong Wang · Wotao Yin · Simon Du · Lin Yang -
2020 Poster: On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems »
Kaiqing Zhang · Bin Hu · Tamer Basar -
2020 Poster: Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension »
Ruosong Wang · Russ Salakhutdinov · Lin Yang -
2020 Poster: CoinDICE: Off-Policy Confidence Interval Estimation »
Bo Dai · Ofir Nachum · Yinlam Chow · Lihong Li · Csaba Szepesvari · Dale Schuurmans -
2020 Poster: A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods »
Yue Wu · Weitong ZHANG · Pan Xu · Quanquan Gu -
2020 Poster: Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design »
Michael Dennis · Natasha Jaques · Eugene Vinitsky · Alexandre Bayen · Stuart Russell · Andrew Critch · Sergey Levine -
2020 Poster: Off-Policy Evaluation via the Regularized Lagrangian »
Mengjiao (Sherry) Yang · Ofir Nachum · Bo Dai · Lihong Li · Dale Schuurmans -
2020 Poster: MOPO: Model-based Offline Policy Optimization »
Tianhe Yu · Garrett Thomas · Lantao Yu · Stefano Ermon · James Zou · Sergey Levine · Chelsea Finn · Tengyu Ma -
2020 Poster: DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction »
Aviral Kumar · Abhishek Gupta · Sergey Levine -
2020 Poster: Robust Multi-Agent Reinforcement Learning with Model Uncertainty »
Kaiqing Zhang · TAO SUN · Yunzhe Tao · Sahika Genc · Sunil Mallya · Tamer Basar -
2020 Poster: Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes »
Dongsheng Ding · Kaiqing Zhang · Tamer Basar · Mihailo Jovanovic -
2020 Poster: On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces »
Zhuoran Yang · Chi Jin · Zhaoran Wang · Mengdi Wang · Michael Jordan -
2020 Poster: Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss »
Shuang Qiu · Xiaohan Wei · Zhuoran Yang · Jieping Ye · Zhaoran Wang -
2020 Poster: Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity »
Kaiqing Zhang · Sham Kakade · Tamer Basar · Lin Yang -
2020 Poster: Provably Good Batch Reinforcement Learning Without Great Exploration »
Yao Liu · Adith Swaminathan · Alekh Agarwal · Emma Brunskill -
2020 Poster: Information Theoretic Regret Bounds for Online Nonlinear Control »
Sham Kakade · Akshay Krishnamurthy · Kendall Lowrey · Motoya Ohnishi · Wen Sun -
2020 Poster: Off-Policy Interval Estimation with Lipschitz Value Iteration »
Ziyang Tang · Yihao Feng · Na Zhang · Jian Peng · Qiang Liu -
2020 Spotlight: Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity »
Kaiqing Zhang · Sham Kakade · Tamer Basar · Lin Yang -
2020 Spotlight: Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning »
Fei Feng · Ruosong Wang · Wotao Yin · Simon Du · Lin Yang -
2020 Spotlight: DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction »
Aviral Kumar · Abhishek Gupta · Sergey Levine -
2020 Spotlight: CoinDICE: Off-Policy Confidence Interval Estimation »
Bo Dai · Ofir Nachum · Yinlam Chow · Lihong Li · Csaba Szepesvari · Dale Schuurmans -
2020 Oral: Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design »
Michael Dennis · Natasha Jaques · Eugene Vinitsky · Alexandre Bayen · Stuart Russell · Andrew Critch · Sergey Levine -
2020 Tutorial: (Track3) Policy Optimization in Reinforcement Learning »
Sham M Kakade · Martha White · Nicolas Le Roux -
2020 Tutorial: (Track3) Offline Reinforcement Learning: From Algorithm Design to Practical Applications »
Sergey Levine · Aviral Kumar -
2020 : Real World RL with Vowpal Wabbit: Beyond Contextual Bandits »
John Langford · Marek Wydmuch · Maryam Majzoubi · Adith Swaminathan · · Dylan Foster · Paul Mineiro -
2019 : Closing Remarks »
Bo Dai · Niao He · Nicolas Le Roux · Lihong Li · Dale Schuurmans · Martha White -
2019 : Logarithmic Regret for Online Control »
Naman Agarwal · Elad Hazan · Karan Singh -
2019 : Contributed Session - Spotlight Talks »
Jonathan Frankle · David Schwab · Ari Morcos · Qianli Ma · Yao-Hung Hubert Tsai · Ruslan Salakhutdinov · YiDing Jiang · Dilip Krishnan · Hossein Mobahi · Samy Bengio · Sho Yaida · Muqiao Yang -
2019 : Continuous Online Learning and New Insights to Online Imitation Learning »
Jonathan Lee · Ching-An Cheng · Ken Goldberg · Byron Boots -
2019 : Poster Presentations »
Rahul Mehta · Andrew Lampinen · Binghong Chen · Sergio Pascual-Diaz · Jordi Grau-Moya · Aldo Faisal · Jonathan Tompson · Yiren Lu · Khimya Khetarpal · Martin Klissarov · Pierre-Luc Bacon · Doina Precup · Thanard Kurutach · Aviv Tamar · Pieter Abbeel · Jinke He · Maximilian Igl · Shimon Whiteson · Wendelin Boehmer · Raphaël Marinier · Olivier Pietquin · Karol Hausman · Sergey Levine · Chelsea Finn · Tianhe Yu · Lisa Lee · Benjamin Eysenbach · Emilio Parisotto · Eric Xing · Ruslan Salakhutdinov · Hongyu Ren · Anima Anandkumar · Deepak Pathak · Christopher Lu · Trevor Darrell · Alexei Efros · Phillip Isola · Feng Liu · Bo Han · Gang Niu · Masashi Sugiyama · Saurabh Kumar · Janith Petangoda · Johan Ferret · James McClelland · Kara Liu · Animesh Garg · Robert Lange -
2019 : Traffic4cast -- Traffic Map Movie Forecasting »
Sepp Hochreiter · Leonid Sigal · Moritz Neun · David Jonietz · Sungbin Choi · Henry Martin · Wei Yu · Zhichen Liu · Tu Nguyen · Pedro Herruzo Sánchez · Xiaoxia Shi · Aleksandra Gruca · Alastair Sutherland · David Kreil · Michael Kopp -
2019 : Contributed talk: On Solving Local Minimax Optimization: A Follow-the-Ridge Approach »
Yuanhao Wang -
2019 : Poster Session »
Matthia Sabatelli · Adam Stooke · Amir Abdi · Paulo Rauber · Leonard Adolphs · Ian Osband · Hardik Meisheri · Karol Kurach · Johannes Ackermann · Matt Benatan · GUO ZHANG · Chen Tessler · Dinghan Shen · Mikayel Samvelyan · Riashat Islam · Murtaza Dalal · Luke Harries · Andrey Kurenkov · Konrad Żołna · Sudeep Dasari · Kristian Hartikainen · Ofir Nachum · Kimin Lee · Markus Holzleitner · Vu Nguyen · Francis Song · Christopher Grimm · Felipe Leno da Silva · Yuping Luo · Yifan Wu · Alex Lee · Thomas Paine · Wei-Yang Qu · Daniel Graves · Yannis Flet-Berliac · Yunhao Tang · Suraj Nair · Matthew Hausknecht · Akhil Bagaria · Simon Schmitt · Bowen Baker · Paavo Parmas · Benjamin Eysenbach · Lisa Lee · Siyu Lin · Daniel Seita · Abhishek Gupta · Riley Simmons-Edler · Yijie Guo · Kevin Corder · Vikash Kumar · Scott Fujimoto · Adam Lerer · Ignasi Clavera Gilaberte · Nicholas Rhinehart · Ashvin Nair · Ge Yang · Lingxiao Wang · Sungryull Sohn · J. Fernando Hernandez-Garcia · Xian Yeow Lee · Rupesh Srivastava · Khimya Khetarpal · Chenjun Xiao · Luckeciano Carvalho Melo · Rishabh Agarwal · Tianhe Yu · Glen Berseth · Devendra Singh Chaplot · Jie Tang · Anirudh Srinivasan · Tharun Kumar Reddy Medini · Aaron Havens · Misha Laskin · Asier Mujika · Rohan Saphal · Joseph Marino · Alex Ray · Joshua Achiam · Ajay Mandlekar · Zhuang Liu · Danijar Hafner · Zhiwen Tang · Ted Xiao · Michael Walton · Jeff Druce · Ferran Alet · Zhang-Wei Hong · Stephanie Chan · Anusha Nagabandi · Hao Liu · Hao Sun · Ge Liu · Dinesh Jayaraman · John Co-Reyes · Sophia Sanborn -
2019 : Oral Presentations »
Janith Petangoda · Sergio Pascual-Diaz · Jordi Grau-Moya · Raphaël Marinier · Olivier Pietquin · Alexei Efros · Phillip Isola · Trevor Darrell · Christopher Lu · Deepak Pathak · Johan Ferret -
2019 : Lunch Break and Posters »
Xingyou Song · Elad Hoffer · Wei-Cheng Chang · Jeremy Cohen · Jyoti Islam · Yaniv Blumenfeld · Andreas Madsen · Jonathan Frankle · Sebastian Goldt · Satrajit Chatterjee · Abhishek Panigrahi · Alex Renda · Brian Bartoldson · Israel Birhane · Aristide Baratin · Niladri Chatterji · Roman Novak · Jessica Forde · YiDing Jiang · Yilun Du · Linara Adilova · Michael Kamp · Berry Weinstein · Itay Hubara · Tal Ben-Nun · Torsten Hoefler · Daniel Soudry · Hsiang-Fu Yu · Kai Zhong · Yiming Yang · Inderjit Dhillon · Jaime Carbonell · Yanqing Zhang · Dar Gilboa · Johannes Brandstetter · Alexander R Johansen · Gintare Karolina Dziugaite · Raghav Somani · Ari Morcos · Freddie Kalaitzis · Hanie Sedghi · Lechao Xiao · John Zech · Muqiao Yang · Simran Kaur · Qianli Ma · Yao-Hung Hubert Tsai · Ruslan Salakhutdinov · Sho Yaida · Zachary Lipton · Daniel Roy · Michael Carbin · Florent Krzakala · Lenka Zdeborová · Guy Gur-Ari · Ethan Dyer · Dilip Krishnan · Hossein Mobahi · Samy Bengio · Behnam Neyshabur · Praneeth Netrapalli · Kris Sankaran · Julien Cornebise · Yoshua Bengio · Vincent Michalski · Samira Ebrahimi Kahou · Md Rifat Arefin · Jiri Hron · Jaehoon Lee · Jascha Sohl-Dickstein · Samuel Schoenholz · David Schwab · Dongyu Li · Sang Keun Choe · Henning Petzka · Ashish Verma · Zhichao Lin · Cristian Sminchisescu -
2019 : Poster Spotlight 2 »
Aaron Sidford · Mengdi Wang · Lin Yang · Yinyu Ye · Zuyue Fu · Zhuoran Yang · Yongxin Chen · Zhaoran Wang · Ofir Nachum · Bo Dai · Ilya Kostrikov · Dale Schuurmans · Ziyang Tang · Yihao Feng · Lihong Li · Denny Zhou · Qiang Liu · Rodrigo Toro Icarte · Ethan Waldie · Toryn Klassen · Rick Valenzano · Margarita Castro · Simon Du · Sham Kakade · Ruosong Wang · Minshuo Chen · Tianyi Liu · Xingguo Li · Zhaoran Wang · Tuo Zhao · Philip Amortila · Doina Precup · Prakash Panangaden · Marc Bellemare -
2019 : Contributed Talks »
Kevin Lu · Matthew Hausknecht · Ofir Nachum -
2019 : Panel Discussion »
Richard Sutton · Doina Precup -
2019 : The Provable Effectiveness of Policy Gradient Methods in Reinforcement Learning »
Sham Kakade -
2019 : Break / Poster Session 1 »
Antonia Marcu · Yao-Yuan Yang · Pascale Gourdeau · Chen Zhu · Thodoris Lykouris · Jianfeng Chi · Mark Kozdoba · Arjun Nitin Bhagoji · Xiaoxia Wu · Jay Nandy · Michael T Smith · Bingyang Wen · Yuege Xie · Konstantinos Pitas · Suprosanna Shit · Maksym Andriushchenko · Dingli Yu · Gaël Letarte · Misha Khodak · Hussein Mozannar · Chara Podimata · James Foulds · Yizhen Wang · Huishuai Zhang · Ondrej Kuzelka · Alexander Levine · Nan Lu · Zakaria Mhammedi · Paul Viallard · Diana Cai · Lovedeep Gondara · James Lucas · Yasaman Mahdaviyeh · Aristide Baratin · Rishi Bommasani · Alessandro Barp · Andrew Ilyas · Kaiwen Wu · Jens Behrmann · Omar Rivasplata · Amir Nazemi · Aditi Raghunathan · Will Stephenson · Sahil Singla · Akhil Gupta · YooJung Choi · Yannic Kilcher · Clare Lyle · Edoardo Manino · Andrew Bennett · Zhi Xu · Niladri Chatterji · Emre Barut · Flavien Prost · Rodrigo Toro Icarte · Arno Blaas · Chulhee Yun · Sahin Lale · YiDing Jiang · Tharun Kumar Reddy Medini · Ashkan Rezaei · Alexander Meinke · Stephen Mell · Gary Kazantsev · Shivam Garg · Aradhana Sinha · Vishnu Lokhande · Geovani Rizk · Han Zhao · Aditya Kumar Akash · Jikai Hou · Ali Ghodsi · Matthias Hein · Tyler Sypherd · Yichen Yang · Anastasia Pentina · Pierre Gillot · Antoine Ledent · Guy Gur-Ari · Noah MacAulay · Tianzong Zhang -
2019 : Bayes-Adaptive Deep Reinforcement Learning via Meta-Learning - Invited Talk »
Shimon Whiteson -
2019 : Poster and Coffee Break 1 »
Aaron Sidford · Aditya Mahajan · Alejandro Ribeiro · Alex Lewandowski · Ali H Sayed · Ambuj Tewari · Angelika Steger · Anima Anandkumar · Asier Mujika · Hilbert J Kappen · Bolei Zhou · Byron Boots · Chelsea Finn · Chen-Yu Wei · Chi Jin · Ching-An Cheng · Christina Yu · Clement Gehring · Craig Boutilier · Dahua Lin · Daniel McNamee · Daniel Russo · David Brandfonbrener · Denny Zhou · Devesh Jha · Diego Romeres · Doina Precup · Dominik Thalmeier · Eduard Gorbunov · Elad Hazan · Elena Smirnova · Elvis Dohmatob · Emma Brunskill · Enrique Munoz de Cote · Ethan Waldie · Florian Meier · Florian Schaefer · Ge Liu · Gergely Neu · Haim Kaplan · Hao Sun · Hengshuai Yao · Jalaj Bhandari · James A Preiss · Jayakumar Subramanian · Jiajin Li · Jieping Ye · Jimmy Smith · Joan Bas Serrano · Joan Bruna · John Langford · Jonathan Lee · Jose A. Arjona-Medina · Kaiqing Zhang · Karan Singh · Yuping Luo · Zafarali Ahmed · Zaiwei Chen · Zhaoran Wang · Zhizhong Li · Zhuoran Yang · Ziping Xu · Ziyang Tang · Yi Mao · David Brandfonbrener · Shirli Di-Castro · Riashat Islam · Zuyue Fu · Abhishek Naik · Saurabh Kumar · Benjamin Petit · Angeliki Kamoutsi · Simone Totaro · Arvind Raghunathan · Rui Wu · Donghwan Lee · Dongsheng Ding · Alec Koppel · Hao Sun · Christian Tjandraatmadja · Mahdi Karami · Jincheng Mei · Chenjun Xiao · Junfeng Wen · Zichen Zhang · Ross Goroshin · Mohammad Pezeshki · Jiaqi Zhai · Philip Amortila · Shuo Huang · Mariya Vasileva · El houcine Bergou · Adel Ahmadyan · Haoran Sun · Sheng Zhang · Lukas Gruber · Yuanhao Wang · Tetiana Parshakova -
2019 : Poster Spotlight 1 »
David Brandfonbrener · Joan Bruna · Tom Zahavy · Haim Kaplan · Yishay Mansour · Nikos Karampatziakis · John Langford · Paul Mineiro · Donghwan Lee · Niao He -
2019 : Adaptive Trust Region Policy Optimization: Convergence and Faster Rates of regularized MDPs »
Lior Shani · Yonathan Efroni · Shie Mannor -
2019 : Unsupervised State Embedding and Aggregation towards Scalable Reinforcement Learning »
Mengdi Wang -
2019 Workshop: Bridging Game Theory and Deep Learning »
Ioannis Mitliagkas · Gauthier Gidel · Niao He · Reyhane Askari Hemmat · N H · Nika Haghtalab · Simon Lacoste-Julien -
2019 Workshop: The Optimization Foundations of Reinforcement Learning »
Bo Dai · Niao He · Nicolas Le Roux · Lihong Li · Dale Schuurmans · Martha White -
2019 : Opening Remarks »
Bo Dai · Niao He · Nicolas Le Roux · Lihong Li · Dale Schuurmans · Martha White -
2019 : Panel Discussion led by Grace Lindsay »
Grace Lindsay · Blake Richards · Doina Precup · Jacqueline Gottlieb · Jeff Clune · Jane Wang · Richard Sutton · Angela Yu · Ida Momennejad -
2019 : Invited Talk #7: Richard Sutton »
Richard Sutton -
2019 : Poster Session »
Jonathan Scarlett · Piotr Indyk · Ali Vakilian · Adrian Weller · Partha P Mitra · Benjamin Aubin · Bruno Loureiro · Florent Krzakala · Lenka Zdeborová · Kristina Monakhova · Joshua Yurtsever · Laura Waller · Hendrik Sommerhoff · Michael Moeller · Rushil Anirudh · Shuang Qiu · Xiaohan Wei · Zhuoran Yang · Jayaraman Thiagarajan · Salman Asif · Michael Gillhofer · Johannes Brandstetter · Sepp Hochreiter · Felix Petersen · Dhruv Patel · Assad Oberai · Akshay Kamath · Sushrut Karmalkar · Eric Price · Ali Ahmed · Zahra Kadkhodaie · Sreyas Mohan · Eero Simoncelli · Carlos Fernandez-Granda · Oscar Leong · Wesam Sakla · Rebecca Willett · Stephan Hoyer · Jascha Sohl-Dickstein · Sam Greydanus · Gauri Jagatap · Chinmay Hegde · Michael Kellman · Jonathan Tamir · Nouamane Laanait · Ousmane Dia · Mirco Ravanelli · Jonathan Binas · Negar Rostamzadeh · Shirin Jalali · Tiantian Fang · Alex Schwing · Sébastien Lachapelle · Philippe Brouillard · Tristan Deleu · Simon Lacoste-Julien · Stella Yu · Arya Mazumdar · Ankit Singh Rawat · Yue Zhao · Jianshu Chen · Xiaoyang Li · Hubert Ramsauer · Gabrio Rizzuti · Nikolaos Mitsakos · Dingzhou Cao · Thomas Strohmer · Yang Li · Pei Peng · Gregory Ongie -
2019 : Coffee/Poster session 2 »
Xingyou Song · Puneet Mangla · David Salinas · Zhenxun Zhuang · Leo Feng · Shell Xu Hu · Raul Puri · Wesley Maddox · Aniruddh Raghu · Prudencio Tossou · Mingzhang Yin · Ishita Dasgupta · Kangwook Lee · Ferran Alet · Zhen Xu · Jörg Franke · James Harrison · Jonathan Warrell · Guneet Dhillon · Arber Zela · Xin Qiu · Julien Niklas Siems · Russell Mendonca · Louis Schlessinger · Jeffrey Li · Georgiana Manolache · Debojyoti Dutta · Lucas Glass · Abhishek Singh · Gregor Koehler -
2019 : Poster Spotlights B (13 posters) »
Alberto Camacho · Chris Percy · Vaishak Belle · Beliz Gunel · Toryn Klassen · Tillman Weyde · Mohamed Ghalwash · Siddhant Arora · León Illanes · Jonathan Raiman · Qing Wang · Alexander Lew · So Yeon Min -
2019 : Poster Session »
Ethan Harris · Tom White · Oh Hyeon Choung · Takashi Shinozaki · Dipan Pal · Katherine L. Hermann · Judy Borowski · Camilo Fosco · Chaz Firestone · Vijay Veerabadran · Benjamin Lahner · Chaitanya Ryali · Fenil Doshi · Pulkit Singh · Sharon Zhou · Michel Besserve · Michael Chang · Anelise Newman · Mahesan Niranjan · Jonathon Hare · Daniela Mihai · Marios Savvides · Simon Kornblith · Christina M Funke · Aude Oliva · Virginia de Sa · Dmitry Krotov · Colin Conwell · George Alvarez · Alex Kolchinski · Shengjia Zhao · Mitchell Gordon · Michael Bernstein · Stefano Ermon · Arash Mehrjou · Bernhard Schölkopf · John Co-Reyes · Michael Janner · Jiajun Wu · Josh Tenenbaum · Sergey Levine · Yalda Mohsenzadeh · Zhenglong Zhou -
2019 : Coffee/Poster session 1 »
Shiro Takagi · Khurram Javed · Johanna Sommer · Amr Sharaf · Pierluca D'Oro · Ying Wei · Sivan Doveh · Colin White · Santiago Gonzalez · Cuong Nguyen · Mao Li · Tianhe Yu · Tiago Ramalho · Masahiro Nomura · Ahsan Alvi · Jean-Francois Ton · W. Ronny Huang · Jessica Lee · Sebastian Flennerhag · Michael Zhang · Abram Friesen · Paul Blomstedt · Alina Dubatovka · Sergey Bartunov · Subin Yi · Iaroslav Shcherbatyi · Christian Simon · Zeyuan Shang · David MacLeod · Lu Liu · Liam Fowl · Diego Mesquita · Deirdre Quillen -
2019 : Poster Session »
Eduard Gorbunov · Alexandre d'Aspremont · Lingxiao Wang · Liwei Wang · Boris Ginsburg · Alessio Quaglino · Camille Castera · Saurabh Adya · Diego Granziol · Rudrajit Das · Raghu Bollapragada · Fabian Pedregosa · Martin Takac · Majid Jahani · Sai Praneeth Karimireddy · Hilal Asi · Balint Daroczy · Leonard Adolphs · Aditya Rawal · Nicolas Brandt · Minhan Li · Giuseppe Ughi · Orlando Romero · Ivan Skorokhodov · Damien Scieur · Kiwook Bae · Konstantin Mishchenko · Rohan Anil · Vatsal Sharan · Aditya Balu · Chao Chen · Zhewei Yao · Tolga Ergen · Paul Grigas · Chris Junchi Li · Jimmy Ba · Stephen J Roberts · Sharan Vaswani · Armin Eftekhari · Chhavi Sharma -
2019 : Robust One-Bit Recovery via ReLU Generative Networks: Improved Statistical Rate and Global Landscape Analysis »
Shuang Qiu · Xiaohan Wei · Zhuoran Yang -
2019 Workshop: Safety and Robustness in Decision-making »
Mohammad Ghavamzadeh · Shie Mannor · Yisong Yue · Marek Petrik · Yinlam Chow -
2019 Poster: Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function »
Aviv Rosenberg · Yishay Mansour -
2019 Poster: An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints »
Mehmet Fatih Sahin · Armin eftekhari · Ahmet Alacaoglu · Fabian Latorre · Volkan Cevher -
2019 Poster: Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks »
Spencer Frei · Yuan Cao · Quanquan Gu -
2019 Poster: Wasserstein Dependency Measure for Representation Learning »
Sherjil Ozair · Corey Lynch · Yoshua Bengio · Aaron van den Oord · Sergey Levine · Pierre Sermanet -
2019 Poster: Towards Understanding the Importance of Shortcut Connections in Residual Networks »
Tianyi Liu · Minshuo Chen · Mo Zhou · Simon Du · Enlu Zhou · Tuo Zhao -
2019 Poster: Statistical-Computational Tradeoff in Single Index Models »
Lingxiao Wang · Zhuoran Yang · Zhaoran Wang -
2019 Poster: Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks »
Difan Zou · Ziniu Hu · Yewen Wang · Song Jiang · Yizhou Sun · Quanquan Gu -
2019 Poster: RUDDER: Return Decomposition for Delayed Rewards »
Jose A. Arjona-Medina · Michael Gillhofer · Michael Widrich · Thomas Unterthiner · Johannes Brandstetter · Sepp Hochreiter -
2019 Poster: Planning with Goal-Conditioned Policies »
Soroush Nasiriany · Vitchyr Pong · Steven Lin · Sergey Levine -
2019 Poster: The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares »
Rong Ge · Sham Kakade · Rahul Kidambi · Praneeth Netrapalli -
2019 Poster: Search on the Replay Buffer: Bridging Planning and Reinforcement Learning »
Benjamin Eysenbach · Russ Salakhutdinov · Sergey Levine -
2019 Poster: A Kernel Loss for Solving the Bellman Equation »
Yihao Feng · Lihong Li · Qiang Liu -
2019 Poster: Splitting Steepest Descent for Growing Neural Architectures »
Lemeng Wu · Dilin Wang · Qiang Liu -
2019 Poster: Convergence of Adversarial Training in Overparametrized Neural Networks »
Ruiqi Gao · Tianle Cai · Haochuan Li · Cho-Jui Hsieh · Liwei Wang · Jason Lee -
2019 Spotlight: Splitting Steepest Descent for Growing Neural Architectures »
Lemeng Wu · Dilin Wang · Qiang Liu -
2019 Spotlight: Convergence of Adversarial Training in Overparametrized Neural Networks »
Ruiqi Gao · Tianle Cai · Haochuan Li · Cho-Jui Hsieh · Liwei Wang · Jason Lee -
2019 Poster: Transferable Normalization: Towards Improving Transferability of Deep Neural Networks »
Ximei Wang · Ying Jin · Mingsheng Long · Jianmin Wang · Michael Jordan -
2019 Poster: MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies »
Xue Bin Peng · Michael Chang · Grace Zhang · Pieter Abbeel · Sergey Levine -
2019 Poster: Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels »
Simon Du · Kangcheng Hou · Russ Salakhutdinov · Barnabas Poczos · Ruosong Wang · Keyulu Xu -
2019 Poster: Equipping Experts/Bandits with Long-term Memory »
Kai Zheng · Haipeng Luo · Ilias Diakonikolas · Liwei Wang -
2019 Poster: MAVEN: Multi-Agent Variational Exploration »
Anuj Mahajan · Tabish Rashid · Mikayel Samvelyan · Shimon Whiteson -
2019 Poster: Stein Variational Gradient Descent With Matrix-Valued Kernels »
Dilin Wang · Ziyang Tang · Chandrajit Bajaj · Qiang Liu -
2019 Poster: Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning »
Gregory Farquhar · Shimon Whiteson · Jakob Foerster -
2019 Poster: Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost »
Zhuoran Yang · Yongxin Chen · Mingyi Hong · Zhaoran Wang -
2019 Poster: Budgeted Reinforcement Learning in Continuous State Space »
Nicolas Carrara · Edouard Leurent · Romain Laroche · Tanguy Urvoy · Odalric-Ambrym Maillard · Olivier Pietquin -
2019 Poster: Multi-Agent Common Knowledge Reinforcement Learning »
Christian Schroeder de Witt · Jakob Foerster · Gregory Farquhar · Philip Torr · Wendelin Boehmer · Shimon Whiteson -
2019 Poster: Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy »
Boyi Liu · Qi Cai · Zhuoran Yang · Zhaoran Wang -
2019 Poster: Exponential Family Estimation via Adversarial Dynamics Embedding »
Bo Dai · Zhen Liu · Hanjun Dai · Niao He · Arthur Gretton · Le Song · Dale Schuurmans -
2019 Poster: Neural Temporal-Difference Learning Converges to Global Optima »
Qi Cai · Zhuoran Yang · Jason Lee · Zhaoran Wang -
2019 Poster: Meta-Inverse Reinforcement Learning with Probabilistic Context Variables »
Lantao Yu · Tianhe Yu · Chelsea Finn · Stefano Ermon -
2019 Poster: Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction »
Aviral Kumar · Justin Fu · George Tucker · Sergey Levine -
2019 Poster: Stochastic Frank-Wolfe for Composite Convex Minimization »
Francesco Locatello · Alp Yurtsever · Olivier Fercoq · Volkan Cevher -
2019 Poster: Graph-based Discriminators: Sample Complexity and Expressiveness »
Roi Livni · Yishay Mansour -
2019 Poster: Unsupervised Curricula for Visual Meta-Reinforcement Learning »
Allan Jabri · Kyle Hsu · Abhishek Gupta · Benjamin Eysenbach · Sergey Levine · Chelsea Finn -
2019 Poster: McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds »
Rui (Ray) Zhang · Xingwu Liu · Yuyi Wang · Liwei Wang -
2019 Spotlight: McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds »
Rui (Ray) Zhang · Xingwu Liu · Yuyi Wang · Liwei Wang -
2019 Spotlight: Graph-based Discriminators: Sample Complexity and Expressiveness »
Roi Livni · Yishay Mansour -
2019 Poster: DAC: The Double Actor-Critic Architecture for Learning Options »
Shangtong Zhang · Shimon Whiteson -
2019 Poster: A Geometric Perspective on Optimal Representations for Reinforcement Learning »
Marc Bellemare · Will Dabney · Robert Dadashi · Adrien Ali Taiga · Pablo Samuel Castro · Nicolas Le Roux · Dale Schuurmans · Tor Lattimore · Clare Lyle -
2019 Poster: Fast Efficient Hyperparameter Tuning for Policy Gradient Methods »
Supratik Paul · Vitaly Kurin · Shimon Whiteson -
2019 Poster: Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network »
Siqi Wang · Yijie Zeng · Xinwang Liu · En Zhu · Jianping Yin · Chuanfu Xu · Marius Kloft -
2019 Poster: Learning to Screen »
Alon Cohen · Avinatan Hassidim · Haim Kaplan · Yishay Mansour · Shay Moran -
2019 Poster: Learning Macroscopic Brain Connectomes via Group-Sparse Factorization »
Farzane Aminmansour · Andrew Patterson · Lei Le · Yisu Peng · Daniel Mitchell · Franco Pestilli · Cesar F Caiafa · Russell Greiner · Martha White -
2019 Poster: Language as an Abstraction for Hierarchical Deep Reinforcement Learning »
YiDing Jiang · Shixiang (Shane) Gu · Kevin Murphy · Chelsea Finn -
2019 Poster: Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks »
Sitao Luan · Mingde Zhao · Xiao-Wen Chang · Doina Precup -
2019 Poster: Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games »
Kaiqing Zhang · Zhuoran Yang · Tamer Basar -
2019 Poster: Compositional Plan Vectors »
Coline Devin · Daniel Geng · Pieter Abbeel · Trevor Darrell · Sergey Levine -
2019 Poster: DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections »
Ofir Nachum · Yinlam Chow · Bo Dai · Lihong Li -
2019 Poster: VIREL: A Variational Inference Framework for Reinforcement Learning »
Mattie Fellows · Anuj Mahajan · Tim G. J. Rudner · Shimon Whiteson -
2019 Poster: Policy Continuation with Hindsight Inverse Dynamics »
Hao Sun · Zhizhong Li · Xiaotong Liu · Bolei Zhou · Dahua Lin -
2019 Poster: Learning Reward Machines for Partially Observable Reinforcement Learning »
Rodrigo Toro Icarte · Ethan Waldie · Toryn Klassen · Rick Valenzano · Margarita Castro · Sheila McIlraith -
2019 Spotlight: Learning Reward Machines for Partially Observable Reinforcement Learning »
Rodrigo Toro Icarte · Ethan Waldie · Toryn Klassen · Rick Valenzano · Margarita Castro · Sheila McIlraith -
2019 Spotlight: Policy Continuation with Hindsight Inverse Dynamics »
Hao Sun · Zhizhong Li · Xiaotong Liu · Bolei Zhou · Dahua Lin -
2019 Spotlight: Unsupervised Curricula for Visual Meta-Reinforcement Learning »
Allan Jabri · Kyle Hsu · Abhishek Gupta · Benjamin Eysenbach · Sergey Levine · Chelsea Finn -
2019 Spotlight: VIREL: A Variational Inference Framework for Reinforcement Learning »
Mattie Fellows · Anuj Mahajan · Tim G. J. Rudner · Shimon Whiteson -
2019 Spotlight: DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections »
Ofir Nachum · Yinlam Chow · Bo Dai · Lihong Li -
2019 Poster: Causal Confusion in Imitation Learning »
Pim de Haan · Dinesh Jayaraman · Sergey Levine -
2019 Poster: Meta-Learning with Implicit Gradients »
Aravind Rajeswaran · Chelsea Finn · Sham Kakade · Sergey Levine -
2019 Poster: Efficient Symmetric Norm Regression via Linear Sketching »
Zhao Song · Ruosong Wang · Lin Yang · Hongyang Zhang · Peilin Zhong -
2019 Poster: Importance Resampling for Off-policy Prediction »
Matthew Schlegel · Wesley Chung · Daniel Graves · Jian Qian · Martha White -
2019 Poster: Meta-Learning Representations for Continual Learning »
Khurram Javed · Martha White -
2019 Poster: Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits »
Yogev Bar-On · Yishay Mansour -
2019 Poster: Convergent Policy Optimization for Safe Reinforcement Learning »
Ming Yu · Zhuoran Yang · Mladen Kolar · Zhaoran Wang -
2019 Poster: Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction »
Difan Zou · Pan Xu · Quanquan Gu -
2019 Poster: State Aggregation Learning from Markov Transition Data »
Yaqi Duan · Zheng Tracy Ke · Mengdi Wang -
2019 Poster: Acceleration via Symplectic Discretization of High-Resolution Differential Equations »
Bin Shi · Simon Du · Weijie Su · Michael Jordan -
2019 Poster: Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem »
DongDong Ge · Haoyue Wang · Zikai Xiong · Yinyu Ye -
2019 Poster: Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle »
Simon Du · Yuping Luo · Ruosong Wang · Hanrui Zhang -
2019 Poster: Non-Cooperative Inverse Reinforcement Learning »
Xiangyuan Zhang · Kaiqing Zhang · Erik Miehling · Tamer Basar -
2019 Poster: Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks »
Yuan Cao · Quanquan Gu -
2019 Poster: When to Trust Your Model: Model-Based Policy Optimization »
Michael Janner · Justin Fu · Marvin Zhang · Sergey Levine -
2019 Poster: Exploration via Hindsight Goal Generation »
Zhizhou Ren · Kefan Dong · Yuan Zhou · Qiang Liu · Jian Peng -
2019 Poster: Implicit Generation and Modeling with Energy Based Models »
Yilun Du · Igor Mordatch -
2019 Poster: UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization »
Ali Kavis · Kfir Y. Levy · Francis Bach · Volkan Cevher -
2019 Poster: Guided Meta-Policy Search »
Russell Mendonca · Abhishek Gupta · Rosen Kralev · Pieter Abbeel · Sergey Levine · Chelsea Finn -
2019 Poster: Fast and Provable ADMM for Learning with Generative Priors »
Fabian Latorre · Armin eftekhari · Volkan Cevher -
2019 Poster: Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies »
Yonathan Efroni · Nadav Merlis · Mohammad Ghavamzadeh · Shie Mannor -
2019 Poster: Learning Positive Functions with Pseudo Mirror Descent »
Yingxiang Yang · Haoxiang Wang · Negar Kiyavash · Niao He -
2019 Spotlight: Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies »
Yonathan Efroni · Nadav Merlis · Mohammad Ghavamzadeh · Shie Mannor -
2019 Spotlight: UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization »
Ali Kavis · Kfir Y. Levy · Francis Bach · Volkan Cevher -
2019 Spotlight: Learning Positive Functions with Pseudo Mirror Descent »
Yingxiang Yang · Haoxiang Wang · Negar Kiyavash · Niao He -
2019 Spotlight: Guided Meta-Policy Search »
Russell Mendonca · Abhishek Gupta · Rosen Kralev · Pieter Abbeel · Sergey Levine · Chelsea Finn -
2019 Spotlight: Fast and Provable ADMM for Learning with Generative Priors »
Fabian Latorre · Armin eftekhari · Volkan Cevher -
2019 Spotlight: Implicit Generation and Modeling with Energy Based Models »
Yilun Du · Igor Mordatch -
2019 Oral: Causal Confusion in Imitation Learning »
Pim de Haan · Dinesh Jayaraman · Sergey Levine -
2019 Poster: Logarithmic Regret for Online Control »
Naman Agarwal · Elad Hazan · Karan Singh -
2019 Poster: Generalized Off-Policy Actor-Critic »
Shangtong Zhang · Wendelin Boehmer · Shimon Whiteson -
2019 Poster: An Improved Analysis of Training Over-parameterized Deep Neural Networks »
Difan Zou · Quanquan Gu -
2019 Poster: Learning low-dimensional state embeddings and metastable clusters from time series data »
Yifan Sun · Yaqi Duan · Hao Gong · Mengdi Wang -
2019 Poster: Provably Efficient Q-Learning with Low Switching Cost »
Yu Bai · Tengyang Xie · Nan Jiang · Yu-Xiang Wang -
2019 Poster: Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds »
Minshuo Chen · Haoming Jiang · Wenjing Liao · Tuo Zhao -
2019 Poster: On Exact Computation with an Infinitely Wide Neural Net »
Sanjeev Arora · Simon Du · Wei Hu · Zhiyuan Li · Russ Salakhutdinov · Ruosong Wang -
2019 Poster: Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks »
Yuan Cao · Quanquan Gu -
2019 Spotlight: Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks »
Yuan Cao · Quanquan Gu -
2019 Spotlight: On Exact Computation with an Infinitely Wide Neural Net »
Sanjeev Arora · Simon Du · Wei Hu · Zhiyuan Li · Russ Salakhutdinov · Ruosong Wang -
2019 Oral: Logarithmic Regret for Online Control »
Naman Agarwal · Elad Hazan · Karan Singh -
2018 : Discussion Panel: Ryan Adams, Nicolas Heess, Leslie Kaelbling, Shie Mannor, Emo Todorov (moderator: Roy Fox) »
Ryan Adams · Nicolas Heess · Leslie Kaelbling · Shie Mannor · Emo Todorov · Roy Fox -
2018 : Hierarchical reinforcement learning for composite-task dialogues »
Lihong Li -
2018 : Poster Session »
Carl Trimbach · Mennatullah Siam · Rodrigo Toro Icarte · Zhongtian Dai · Sheila McIlraith · Matthew Rahtz · Robert Sheline · Christopher MacLellan · Carolin Lawrence · Stefan Riezler · Dylan Hadfield-Menell · Fang-I Hsiao -
2018 : Spotlights 2 »
Aditya Gopalan · Sungjoon Choi · Thomas Ringstrom · Roy Fox · Jonas Degrave · Xiya Cao · Karl Pertsch · Maximilian Igl · Brian Ichter -
2018 : Meta-Learning to Follow Instructions, Examples, and Demonstrations »
Sergey Levine -
2018 : Teaching Multiple Tasks to an RL Agent using LTL »
Rodrigo Toro Icarte · Sheila McIlraith -
2018 : Spotlight Talks I »
Juan Leni · Michael Spranger · Ben Bogin · Shane Steinert-Threlkeld · Nicholas Tomlin · Fushan Li · Michael Noukhovitch · Tushar Jain · Jason Lee · Yen-Ling Kuo · Josefina Correa · Karol Hausman -
2018 : TBA 2 »
Sergey Levine -
2018 : Control as Inference and Soft Deep RL (Sergey Levine) »
Sergey Levine -
2018 : Opening Remarks »
Roy Fox -
2018 Workshop: Infer to Control: Probabilistic Reinforcement Learning and Structured Control »
Leslie Kaelbling · Martin Riedmiller · Marc Toussaint · Igor Mordatch · Roy Fox · Tuomas Haarnoja -
2018 Workshop: Wordplay: Reinforcement and Language Learning in Text-based Games »
Adam Trischler · Angeliki Lazaridou · Yonatan Bisk · Wendy Tay · Nate Kushman · Marc-Alexandre Côté · Alessandro Sordoni · Daniel Ricks · Tom Zahavy · Hal Daumé III -
2018 : Invited Speaker #6 Martha White »
Martha White -
2018 : TBC 9 »
Sergey Levine -
2018 : Lunch & Posters »
Haytham Fayek · German Parisi · Brian Xu · Pramod Kaushik Mudrakarta · Sophie Cerf · Sarah Wassermann · Davit Soselia · Rahaf Aljundi · Mohamed Elhoseiny · Frantzeska Lavda · Kevin J Liang · Arslan Chaudhry · Sanmit Narvekar · Vincenzo Lomonaco · Wesley Chung · Michael Chang · Ying Zhao · Zsolt Kira · Pouya Bashivan · Banafsheh Rafiee · Oleksiy Ostapenko · Andrew Jones · Christos Kaplanis · Sinan Kalkan · Dan Teng · Xu He · Vincent Liu · Somjit Nath · Sungsoo Ahn · Ting Chen · Shenyang Huang · Yash Chandak · Nathan Sprague · Martin Schrimpf · Tony Kendall · Jonathan Richard Schwarz · Michael Li · Yunshu Du · Yen-Chang Hsu · Samira Abnar · Bo Wang -
2018 : Finding Mixed Nash Equilibria of Generative Adversarial Networks »
Volkan Cevher -
2018 : Smooth Games in Machine Learning Beyond GANs »
Niao He -
2018 : Poster Session 1 »
Stefan Gadatsch · Danil Kuzin · Navneet Kumar · Patrick Dallaire · Tom Ryder · Remus-Petru Pop · Nathan Hunt · Adam Kortylewski · Sophie Burkhardt · Mahmoud Elnaggar · Dieterich Lawson · Yifeng Li · Jongha (Jon) Ryu · Juhan Bae · Micha Livne · Tim Pearce · Mariia Vladimirova · Jason Ramapuram · Jiaming Zeng · Xinyu Hu · Jiawei He · Danielle Maddix · Arunesh Mittal · Albert Shaw · Tuan Anh Le · Alexander Sagel · Lisha Chen · Victor Gallego · Mahdi Karami · Zihao Zhang · Tal Kachman · Noah Weber · Matt Benatan · Kumar K Sricharan · Vincent Cartillier · Ivan Ovinnikov · Buu Phan · Mahmoud Hossam · Liu Ziyin · Valerii Kharitonov · Eugene Golikov · Qiang Zhang · Jae Myung Kim · Sebastian Farquhar · Jishnu Mukhoti · Xu Hu · Gregory Gundersen · Lavanya Sita Tekumalla · Paris Perdikaris · Ershad Banijamali · Siddhartha Jain · Ge Liu · Martin Gottwald · Katy Blumer · Sukmin Yun · Ranganath Krishnan · Roman Novak · Yilun Du · Yu Gong · Beliz Gokkaya · Jessica Ai · Daniel Duckworth · Johannes von Oswald · Christian Henning · Louis-Philippe Morency · Ali Ghodsi · Mahesh Subedar · Jean-Pascal Pfister · Rémi Lebret · Chao Ma · Aleksander Wieczorek · Laurence Perreault Levasseur -
2018 Workshop: Visually grounded interaction and language »
Florian Strub · Harm de Vries · Erik Wijmans · Samyak Datta · Ethan Perez · Mateusz Malinowski · Stefan Lee · Peter Anderson · Aaron Courville · Jeremie MARY · Dhruv Batra · Devi Parikh · Olivier Pietquin · Chiori HORI · Tim Marks · Anoop Cherian -
2018 Poster: Supervised autoencoders: Improving generalization performance with unsupervised regularizers »
Lei Le · Andrew Patterson · Martha White -
2018 Poster: Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization »
Minshuo Chen · Lin Yang · Mengdi Wang · Tuo Zhao -
2018 Poster: TopRank: A practical algorithm for online stochastic ranking »
Tor Lattimore · Branislav Kveton · Shuai Li · Csaba Szepesvari -
2018 Poster: Differentially Private Contextual Linear Bandits »
Roshan Shariff · Or Sheffet -
2018 Poster: Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima »
Yaodong Yu · Pan Xu · Quanquan Gu -
2018 Poster: Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models »
Kurtland Chua · Roberto Calandra · Rowan McAllister · Sergey Levine -
2018 Poster: A Smoother Way to Train Structured Prediction Models »
Krishna Pillutla · Vincent Roulet · Sham Kakade · Zaid Harchaoui -
2018 Poster: Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning »
Yonathan Efroni · Gal Dalal · Bruno Scherrer · Shie Mannor -
2018 Poster: Online Adaptive Methods, Universality and Acceleration »
Kfir Y. Levy · Alp Yurtsever · Volkan Cevher -
2018 Poster: Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation »
Kush Bhatia · Aldo Pacchiano · Nicolas Flammarion · Peter Bartlett · Michael Jordan -
2018 Poster: Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation »
Liwei Wang · Lunjia Hu · Jiayuan Gu · Zhiqiang Hu · Yue Wu · Kun He · John Hopcroft -
2018 Spotlight: Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation »
Liwei Wang · Lunjia Hu · Jiayuan Gu · Zhiqiang Hu · Yue Wu · Kun He · John Hopcroft -
2018 Spotlight: Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models »
Kurtland Chua · Roberto Calandra · Rowan McAllister · Sergey Levine -
2018 Spotlight: Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning »
Yonathan Efroni · Gal Dalal · Bruno Scherrer · Shie Mannor -
2018 Poster: Mirrored Langevin Dynamics »
Ya-Ping Hsieh · Ali Kavis · Paul Rolland · Volkan Cevher -
2018 Poster: Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization »
Pan Xu · Jinghui Chen · Difan Zou · Quanquan Gu -
2018 Poster: Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification »
Harsh Shrivastava · Eugene Bart · Bob Price · Hanjun Dai · Bo Dai · Srinivas Aluru -
2018 Poster: Variational Inference with Tail-adaptive f-Divergence »
Dilin Wang · Hao Liu · Qiang Liu -
2018 Poster: Provable Gaussian Embedding with One Observation »
Ming Yu · Zhuoran Yang · Tuo Zhao · Mladen Kolar · Zhaoran Wang -
2018 Poster: Online Learning of Quantum States »
Scott Aaronson · Xinyi Chen · Elad Hazan · Satyen Kale · Ashwin Nayak -
2018 Poster: Probabilistic Model-Agnostic Meta-Learning »
Chelsea Finn · Kelvin Xu · Sergey Levine -
2018 Poster: Theoretical guarantees for EM under misspecified Gaussian mixture models »
Raaz Dwivedi · nhật Hồ · Koulik Khamaru · Martin Wainwright · Michael Jordan -
2018 Spotlight: Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization »
Pan Xu · Jinghui Chen · Difan Zou · Quanquan Gu -
2018 Spotlight: Mirrored Langevin Dynamics »
Ya-Ping Hsieh · Ali Kavis · Paul Rolland · Volkan Cevher -
2018 Oral: Variational Inference with Tail-adaptive f-Divergence »
Dilin Wang · Hao Liu · Qiang Liu -
2018 Poster: Representation Balancing MDPs for Off-policy Policy Evaluation »
Yao Liu · Omer Gottesman · Aniruddh Raghu · Matthieu Komorowski · Aldo Faisal · Finale Doshi-Velez · Emma Brunskill -
2018 Poster: Stochastic Cubic Regularization for Fast Nonconvex Optimization »
Nilesh Tripuraneni · Mitchell Stern · Chi Jin · Jeffrey Regier · Michael Jordan -
2018 Poster: Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates »
Krishnakumar Balasubramanian · Saeed Ghadimi -
2018 Poster: Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning »
Tom Zahavy · Matan Haroush · Nadav Merlis · Daniel J Mankowitz · Shie Mannor -
2018 Poster: Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization »
Dongruo Zhou · Pan Xu · Quanquan Gu -
2018 Poster: Context-dependent upper-confidence bounds for directed exploration »
Raksha Kumaraswamy · Matthew Schlegel · Adam White · Martha White -
2018 Poster: On the Local Minima of the Empirical Risk »
Chi Jin · Lydia T. Liu · Rong Ge · Michael Jordan -
2018 Poster: Meta-Reinforcement Learning of Structured Exploration Strategies »
Abhishek Gupta · Russell Mendonca · YuXuan Liu · Pieter Abbeel · Sergey Levine -
2018 Poster: Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation »
Qiang Liu · Lihong Li · Ziyang Tang · Denny Zhou -
2018 Poster: Adversarially Robust Optimization with Gaussian Processes »
Ilija Bogunovic · Jonathan Scarlett · Stefanie Jegelka · Volkan Cevher -
2018 Poster: A Lyapunov-based Approach to Safe Reinforcement Learning »
Yinlam Chow · Ofir Nachum · Edgar Duenez-Guzman · Mohammad Ghavamzadeh -
2018 Poster: Visual Reinforcement Learning with Imagined Goals »
Ashvin Nair · Vitchyr Pong · Murtaza Dalal · Shikhar Bahl · Steven Lin · Sergey Levine -
2018 Poster: Coupled Variational Bayes via Optimization Embedding »
Bo Dai · Hanjun Dai · Niao He · Weiyang Liu · Zhen Liu · Jianshu Chen · Lin Xiao · Le Song -
2018 Spotlight: Adversarially Robust Optimization with Gaussian Processes »
Ilija Bogunovic · Jonathan Scarlett · Stefanie Jegelka · Volkan Cevher -
2018 Spotlight: Visual Reinforcement Learning with Imagined Goals »
Ashvin Nair · Vitchyr Pong · Murtaza Dalal · Shikhar Bahl · Steven Lin · Sergey Levine -
2018 Spotlight: On the Local Minima of the Empirical Risk »
Chi Jin · Lydia T. Liu · Rong Ge · Michael Jordan -
2018 Spotlight: Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization »
Dongruo Zhou · Pan Xu · Quanquan Gu -
2018 Oral: Stochastic Cubic Regularization for Fast Nonconvex Optimization »
Nilesh Tripuraneni · Mitchell Stern · Chi Jin · Jeffrey Regier · Michael Jordan -
2018 Spotlight: Meta-Reinforcement Learning of Structured Exploration Strategies »
Abhishek Gupta · Russell Mendonca · YuXuan Liu · Pieter Abbeel · Sergey Levine -
2018 Spotlight: Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation »
Qiang Liu · Lihong Li · Ziyang Tang · Denny Zhou -
2018 Poster: An Off-policy Policy Gradient Theorem Using Emphatic Weightings »
Ehsan Imani · Eric Graves · Martha White -
2018 Poster: A Neural Compositional Paradigm for Image Captioning »
Bo Dai · Sanja Fidler · Dahua Lin -
2018 Poster: Visual Memory for Robust Path Following »
Ashish Kumar · Saurabh Gupta · David Fouhey · Sergey Levine · Jitendra Malik -
2018 Poster: A Block Coordinate Ascent Algorithm for Mean-Variance Optimization »
Tengyang Xie · Bo Liu · Yangyang Xu · Mohammad Ghavamzadeh · Yinlam Chow · Daoming Lyu · Daesub Yoon -
2018 Poster: Learning to Exploit Stability for 3D Scene Parsing »
Yilun Du · Zhijian Liu · Hector Basevi · Ales Leonardis · Bill Freeman · Josh Tenenbaum · Jiajun Wu -
2018 Poster: Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization »
Tianyi Liu · Shiyang Li · Jianping Shi · Enlu Zhou · Tuo Zhao -
2018 Poster: Is Q-Learning Provably Efficient? »
Chi Jin · Zeyuan Allen-Zhu · Sebastien Bubeck · Michael Jordan -
2018 Poster: Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model »
Aaron Sidford · Mengdi Wang · Xian Wu · Lin Yang · Yinyu Ye -
2018 Poster: Efficient Online Portfolio with Logarithmic Regret »
Haipeng Luo · Chen-Yu Wei · Kai Zheng -
2018 Poster: Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition »
Justin Fu · Avi Singh · Dibya Ghosh · Larry Yang · Sergey Levine -
2018 Poster: Stein Variational Gradient Descent as Moment Matching »
Qiang Liu · Dilin Wang -
2018 Poster: Predictive Approximate Bayesian Computation via Saddle Points »
Yingxiang Yang · Bo Dai · Negar Kiyavash · Niao He -
2018 Spotlight: Efficient Online Portfolio with Logarithmic Regret »
Haipeng Luo · Chen-Yu Wei · Kai Zheng -
2018 Oral: Visual Memory for Robust Path Following »
Ashish Kumar · Saurabh Gupta · David Fouhey · Sergey Levine · Jitendra Malik -
2018 Poster: Data-Efficient Hierarchical Reinforcement Learning »
Ofir Nachum · Shixiang (Shane) Gu · Honglak Lee · Sergey Levine -
2018 Poster: Adversarial Attacks on Stochastic Bandits »
Kwang-Sung Jun · Lihong Li · Yuzhe Ma · Jerry Zhu -
2018 Poster: Data center cooling using model-predictive control »
Nevena Lazic · Craig Boutilier · Tyler Lu · Eehern Wong · Binz Roy · Moonkyung Ryu · Greg Imwalle -
2018 Poster: Information Constraints on Auto-Encoding Variational Bayes »
Romain Lopez · Jeffrey Regier · Michael Jordan · Nir Yosef -
2018 Poster: Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization »
Bargav Jayaraman · Lingxiao Wang · David Evans · Quanquan Gu -
2018 Poster: Provably Correct Automatic Sub-Differentiation for Qualified Programs »
Sham Kakade · Jason Lee -
2018 Poster: Quadratic Decomposable Submodular Function Minimization »
Pan Li · Niao He · Olgica Milenkovic -
2018 Poster: FRAGE: Frequency-Agnostic Word Representation »
Chengyue Gong · Di He · Xu Tan · Tao Qin · Liwei Wang · Tie-Yan Liu -
2018 Poster: Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior »
Sid Reddy · Anca Dragan · Sergey Levine -
2018 Poster: Conditional Adversarial Domain Adaptation »
Mingsheng Long · ZHANGJIE CAO · Jianmin Wang · Michael Jordan -
2018 Poster: Generalized Zero-Shot Learning with Deep Calibration Network »
Shichen Liu · Mingsheng Long · Jianmin Wang · Michael Jordan -
2017 : Panel Discussion »
Matt Botvinick · Emma Brunskill · Marcos Campos · Jan Peters · Doina Precup · David Silver · Josh Tenenbaum · Roy Fox -
2017 : Opening Remarks »
Roy Fox -
2017 Workshop: Hierarchical Reinforcement Learning »
Andrew G Barto · Doina Precup · Shie Mannor · Tom Schaul · Roy Fox · Carlos Florensa -
2017 Workshop: Workshop on Meta-Learning »
Roberto Calandra · Frank Hutter · Hugo Larochelle · Sergey Levine -
2017 : Panel Discussion »
Felix Hill · Olivier Pietquin · Jack Gallant · Raymond Mooney · Sanja Fidler · Chen Yu · Devi Parikh -
2017 : Marius Kloft (Kaiserslautern) on Generalization Error Bounds for Extreme Multi-class Classification »
Marius Kloft -
2017 : Poster session 2 and coffee break »
Sean McGregor · Tobias Hagge · Markus Stoye · Trang Thi Minh Pham · Seungkyun Hong · Amir Farbin · Sungyong Seo · Susana Zoghbi · Daniel George · Stanislav Fort · Steven Farrell · Arthur Pajot · Kyle Pearson · Adam McCarthy · Cecile Germain · Dustin Anderson · Mario Lezcano Casado · Mayur Mudigonda · Benjamin Nachman · Luke de Oliveira · Li Jing · Lingge Li · Soo Kyung Kim · Timothy Gebhard · Tom Zahavy -
2017 : Dialogue systems and RL: interconnecting language, vision and rewards »
Olivier Pietquin -
2017 : Poster session 1 and coffee break »
Tobias Hagge · Sean McGregor · Markus Stoye · Trang Thi Minh Pham · Seungkyun Hong · Amir Farbin · Sungyong Seo · Susana Zoghbi · Daniel George · Stanislav Fort · Steven Farrell · Arthur Pajot · Kyle Pearson · Adam McCarthy · Cecile Germain · Dustin Anderson · Mario Lezcano Casado · Mayur Mudigonda · Benjamin Nachman · Luke de Oliveira · Li Jing · Lingge Li · Soo Kyung Kim · Timothy Gebhard · Tom Zahavy -
2017 Workshop: From 'What If?' To 'What Next?' : Causal Inference and Machine Learning for Intelligent Decision Making »
Ricardo Silva · Panagiotis Toulis · John Shawe-Taylor · Alexander Volfovsky · Thorsten Joachims · Lihong Li · Nathan Kallus · Adith Swaminathan -
2017 Workshop: Extreme Classification: Multi-class & Multi-label Learning in Extremely Large Label Spaces »
Manik Varma · Marius Kloft · Krzysztof Dembczynski -
2017 Workshop: Visually grounded interaction and language »
Florian Strub · Harm de Vries · Abhishek Das · Satwik Kottur · Stefan Lee · Mateusz Malinowski · Olivier Pietquin · Devi Parikh · Dhruv Batra · Aaron Courville · Jeremie Mary -
2017 Poster: Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets »
Karol Hausman · Yevgen Chebotar · Stefan Schaal · Gaurav Sukhatme · Joseph Lim -
2017 Poster: Online Convex Optimization with Stochastic Constraints »
Hao Yu · Michael Neely · Xiaohan Wei -
2017 Poster: Fast Black-box Variational Inference through Stochastic Trust-Region Optimization »
Jeffrey Regier · Michael Jordan · Jon McAuliffe -
2017 Poster: EX2: Exploration with Exemplar Models for Deep Reinforcement Learning »
Justin Fu · John Co-Reyes · Sergey Levine -
2017 Poster: Bridging the Gap Between Value and Policy Based Reinforcement Learning »
Ofir Nachum · Mohammad Norouzi · Kelvin Xu · Dale Schuurmans -
2017 Poster: Rotting Bandits »
Nir Levine · Yacov Crammer · Shie Mannor -
2017 Poster: Is the Bellman residual a bad proxy? »
Matthieu Geist · Bilal Piot · Olivier Pietquin -
2017 Poster: Dynamic-Depth Context Tree Weighting »
Joao V Messias · Shimon Whiteson -
2017 Poster: Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach »
Slobodan Mitrovic · Ilija Bogunovic · Ashkan Norouzi-Fard · Jakub M Tarnawski · Volkan Cevher -
2017 Poster: Online Learning for Multivariate Hawkes Processes »
Yingxiang Yang · Jalal Etesami · Niao He · Negar Kiyavash -
2017 Poster: Online control of the false discovery rate with decaying memory »
Aaditya Ramdas · Fanny Yang · Martin Wainwright · Michael Jordan -
2017 Spotlight: EX2: Exploration with Exemplar Models for Deep Reinforcement Learning »
Justin Fu · John Co-Reyes · Sergey Levine -
2017 Spotlight: Fast Black-box Variational Inference through Stochastic Trust-Region Optimization »
Jeffrey Regier · Michael Jordan · Jon McAuliffe -
2017 Oral: Online control of the false discovery rate with decaying memory »
Aaditya Ramdas · Fanny Yang · Martin Wainwright · Michael Jordan -
2017 Demonstration: Deep Robotic Learning using Visual Imagination and Meta-Learning »
Chelsea Finn · Frederik Ebert · Tianhe Yu · Annie Xie · Sudeep Dasari · Pieter Abbeel · Sergey Levine -
2017 Poster: Decoding with Value Networks for Neural Machine Translation »
Di He · Hanqing Lu · Yingce Xia · Tao Qin · Liwei Wang · Tie-Yan Liu -
2017 Poster: Diffusion Approximations for Online Principal Component Estimation and Global Convergence »
Chris Junchi Li · Mengdi Wang · Tong Zhang -
2017 Poster: Contrastive Learning for Image Captioning »
Bo Dai · Dahua Lin -
2017 Poster: Learning Overcomplete HMMs »
Vatsal Sharan · Sham Kakade · Percy Liang · Gregory Valiant -
2017 Poster: Gradient Descent Can Take Exponential Time to Escape Saddle Points »
Simon Du · Chi Jin · Jason D Lee · Michael Jordan · Aarti Singh · Barnabas Poczos -
2017 Poster: Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data »
Joel A Tropp · Alp Yurtsever · Madeleine Udell · Volkan Cevher -
2017 Poster: Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization »
Pan Xu · Jian Ma · Quanquan Gu -
2017 Poster: Estimation of the covariance structure of heavy-tailed distributions »
Xiaohan Wei · Stanislav Minsker -
2017 Poster: Near Optimal Sketching of Low-Rank Tensor Regression »
Xingguo Li · Jarvis Haupt · David Woodruff -
2017 Poster: Deep Hyperspherical Learning »
Weiyang Liu · Yan-Ming Zhang · Xingguo Li · Zhiding Yu · Bo Dai · Tuo Zhao · Le Song -
2017 Poster: Modulating early visual processing by language »
Harm de Vries · Florian Strub · Jeremie Mary · Hugo Larochelle · Olivier Pietquin · Aaron Courville -
2017 Spotlight: Modulating early visual processing by language »
Harm de Vries · Florian Strub · Jeremie Mary · Hugo Larochelle · Olivier Pietquin · Aaron Courville -
2017 Spotlight: Deep Hyperspherical Learning »
Weiyang Liu · Yan-Ming Zhang · Xingguo Li · Zhiding Yu · Bo Dai · Tuo Zhao · Le Song -
2017 Spotlight: Gradient Descent Can Take Exponential Time to Escape Saddle Points »
Simon Du · Chi Jin · Jason D Lee · Michael Jordan · Aarti Singh · Barnabas Poczos -
2017 Oral: Diffusion Approximations for Online Principal Component Estimation and Global Convergence »
Chris Junchi Li · Mengdi Wang · Tong Zhang -
2017 Poster: Phase Transitions in the Pooled Data Problem »
Jonathan Scarlett · Volkan Cevher -
2017 Poster: Non-convex Finite-Sum Optimization Via SCSG Methods »
Lihua Lei · Cheng Ju · Jianbo Chen · Michael Jordan -
2017 Poster: On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning »
Xingguo Li · Lin Yang · Jason Ge · Jarvis Haupt · Tong Zhang · Tuo Zhao -
2017 Poster: Shallow Updates for Deep Reinforcement Learning »
Nir Levine · Tom Zahavy · Daniel J Mankowitz · Aviv Tamar · Shie Mannor -
2017 Poster: Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning »
Shixiang (Shane) Gu · Timothy Lillicrap · Richard Turner · Zoubin Ghahramani · Bernhard Schölkopf · Sergey Levine -
2017 Poster: Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes »
Jianshu Chen · Chong Wang · Lin Xiao · Ji He · Lihong Li · Li Deng -
2017 Poster: Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization »
Ahmet Alacaoglu · Quoc Tran Dinh · Olivier Fercoq · Volkan Cevher -
2017 Poster: The Expressive Power of Neural Networks: A View from the Width »
Zhou Lu · Hongming Pu · Feicheng Wang · Zhiqiang Hu · Liwei Wang -
2017 Poster: Towards Generalization and Simplicity in Continuous Control »
Aravind Rajeswaran · Kendall Lowrey · Emanuel Todorov · Sham Kakade -
2017 Poster: Kernel Feature Selection via Conditional Covariance Minimization »
Jianbo Chen · Mitchell Stern · Martin J Wainwright · Michael Jordan -
2016 : Richard Sutton (University of Alberta) »
Richard Sutton -
2016 Workshop: Deep Learning for Action and Interaction »
Chelsea Finn · Raia Hadsell · David Held · Sergey Levine · Percy Liang -
2016 Workshop: OPT 2016: Optimization for Machine Learning »
Suvrit Sra · Francis Bach · Sashank J. Reddi · Niao He -
2016 Workshop: Let's Discuss: Learning Methods for Dialogue »
Hal Daumé III · Paul Mineiro · Amanda Stent · Jason E Weston -
2016 : Sergey Levine (University of California, Berkeley) »
Sergey Levine -
2016 : Invited Talk: Olivier Pietquin »
Olivier Pietquin -
2016 : Robust Learning and Inference »
Yishay Mansour -
2016 : Rich Sutton »
Richard Sutton -
2016 : Learning to Communicate with Deep Multi−Agent Reinforcement Learning »
Shimon Whiteson -
2016 Workshop: Advances in Approximate Bayesian Inference »
Tamara Broderick · Stephan Mandt · James McInerney · Dustin Tran · David Blei · Kevin Murphy · Andrew Gelman · Michael I Jordan -
2016 Poster: Online Pricing with Strategic and Patient Buyers »
Michal Feldman · Tomer Koren · Roi Livni · Yishay Mansour · Aviv Zohar -
2016 Poster: An Efficient Streaming Algorithm for the Submodular Cover Problem »
Ashkan Norouzi-Fard · Abbas Bazzi · Ilija Bogunovic · Marwa El Halabi · Ya-Ping Hsieh · Volkan Cevher -
2016 Poster: Unifying Count-Based Exploration and Intrinsic Motivation »
Marc Bellemare · Sriram Srinivasan · Georg Ostrovski · Tom Schaul · David Saxton · Remi Munos -
2016 Poster: Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation »
Ilija Bogunovic · Jonathan Scarlett · Andreas Krause · Volkan Cevher -
2016 Poster: Cyclades: Conflict-free Asynchronous Machine Learning »
Xinghao Pan · Maximilian Lam · Stephen Tu · Dimitris Papailiopoulos · Ce Zhang · Michael Jordan · Kannan Ramchandran · Christopher Ré · Benjamin Recht -
2016 Poster: Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent »
Chi Jin · Sham Kakade · Praneeth Netrapalli -
2016 Poster: Accelerating Stochastic Composition Optimization »
Mengdi Wang · Ji Liu · Ethan Fang -
2016 Poster: Dual Learning for Machine Translation »
Di He · Yingce Xia · Tao Qin · Liwei Wang · Nenghai Yu · Tie-Yan Liu · Wei-Ying Ma -
2016 Poster: Unsupervised Domain Adaptation with Residual Transfer Networks »
Mingsheng Long · Han Zhu · Jianmin Wang · Michael Jordan -
2016 Poster: Statistical Inference for Pairwise Graphical Models Using Score Matching »
Ming Yu · Mladen Kolar · Varun Gupta -
2016 Poster: Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences »
Chi Jin · Yuchen Zhang · Sivaraman Balakrishnan · Martin J Wainwright · Michael Jordan -
2016 Poster: Semiparametric Differential Graph Models »
Pan Xu · Quanquan Gu -
2016 Poster: Value Iteration Networks »
Aviv Tamar · Sergey Levine · Pieter Abbeel · YI WU · Garrett Thomas -
2016 Oral: Value Iteration Networks »
Aviv Tamar · Sergey Levine · Pieter Abbeel · YI WU · Garrett Thomas -
2016 Poster: Learning to Communicate with Deep Multi-Agent Reinforcement Learning »
Jakob Foerster · Yannis Assael · Nando de Freitas · Shimon Whiteson -
2016 Poster: Active Learning with Oracle Epiphany »
Tzu-Kuo Huang · Lihong Li · Ara Vartanian · Saleema Amershi · Jerry Zhu -
2016 Poster: Estimating the class prior and posterior from noisy positives and unlabeled data »
Shantanu Jain · Martha White · Predrag Radivojac -
2016 Poster: Stochastic Three-Composite Convex Minimization »
Alp Yurtsever · Bang Cong Vu · Volkan Cevher -
2016 Poster: Adaptive Skills Adaptive Partitions (ASAP) »
Daniel J Mankowitz · Timothy A Mann · Shie Mannor -
2016 Poster: Safe and Efficient Off-Policy Reinforcement Learning »
Remi Munos · Tom Stepleton · Anna Harutyunyan · Marc Bellemare -
2015 Workshop: Learning, Inference and Control of Multi-Agent Systems »
Vicenç Gómez · Gerhard Neumann · Jonathan S Yedidia · Peter Stone -
2015 : Between stochastic and adversarial: forecasting with online ARMA models »
Shie Mannor -
2015 : Deep Robotic Learning »
Sergey Levine -
2015 : Machine Learning For Conversational Systems »
Larry Heck · Li Deng · Olivier Pietquin · Tomas Mikolov -
2015 Workshop: Machine Learning for (e-)Commerce »
Esteban Arcaute · Mohammad Ghavamzadeh · Shie Mannor · Georgios Theocharous -
2015 Poster: Copeland Dueling Bandits »
Masrour Zoghi · Zohar Karnin · Shimon Whiteson · Maarten de Rijke -
2015 Poster: Online Learning for Adversaries with Memory: Price of Past Mistakes »
Oren Anava · Elad Hazan · Shie Mannor -
2015 Poster: Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach »
Yinlam Chow · Aviv Tamar · Shie Mannor · Marco Pavone -
2015 Poster: Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms »
Yunwen Lei · Urun Dogan · Alexander Binder · Marius Kloft -
2015 Poster: Convergence Rates of Active Learning for Maximum Likelihood Estimation »
Kamalika Chaudhuri · Sham Kakade · Praneeth Netrapalli · Sujay Sanghavi -
2015 Poster: Variational Consensus Monte Carlo »
Maxim Rabinovich · Elaine Angelino · Michael Jordan -
2015 Poster: Policy Gradient for Coherent Risk Measures »
Aviv Tamar · Yinlam Chow · Mohammad Ghavamzadeh · Shie Mannor -
2015 Poster: High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality »
Zhaoran Wang · Quanquan Gu · Yang Ning · Han Liu -
2015 Poster: A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements »
Qinqing Zheng · John Lafferty -
2015 Poster: Fast Bidirectional Probability Estimation in Markov Models »
Siddhartha Banerjee · Peter Lofgren -
2015 Poster: On the Accuracy of Self-Normalized Log-Linear Models »
Jacob Andreas · Maxim Rabinovich · Michael Jordan · Dan Klein -
2015 Poster: Community Detection via Measure Space Embedding »
Mark Kozdoba · Shie Mannor -
2015 Poster: Preconditioned Spectral Descent for Deep Learning »
David Carlson · Edo Collins · Ya-Ping Hsieh · Lawrence Carin · Volkan Cevher -
2015 Poster: Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm »
Qinqing Zheng · Ryota Tomioka -
2015 Poster: A Universal Primal-Dual Convex Optimization Framework »
Alp Yurtsever · Quoc Tran Dinh · Volkan Cevher -
2015 Poster: Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes »
Ryan Giordano · Tamara Broderick · Michael Jordan -
2015 Poster: Super-Resolution Off the Grid »
Qingqing Huang · Sham Kakade -
2015 Spotlight: Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes »
Ryan Giordano · Tamara Broderick · Michael Jordan -
2015 Spotlight: Super-Resolution Off the Grid »
Qingqing Huang · Sham Kakade -
2015 Poster: Basis refinement strategies for linear value function approximation in MDPs »
Gheorghe Comanici · Doina Precup · Prakash Panangaden -
2014 Workshop: Advances in Variational Inference »
David Blei · Shakir Mohamed · Michael Jordan · Charles Blundell · Tamara Broderick · Matthew D. Hoffman -
2014 Workshop: Novel Trends and Applications in Reinforcement Learning »
Csaba Szepesvari · Marc Deisenroth · Sergey Levine · Pedro Ortega · Brian Ziebart · Emma Brunskill · Naftali Tishby · Gerhard Neumann · Daniel Lee · Sridhar Mahadevan · Pieter Abbeel · David Silver · Vicenç Gómez -
2014 Workshop: Second Workshop on Transfer and Multi-Task Learning: Theory meets Practice »
Urun Dogan · Tatiana Tommasi · Yoshua Bengio · Francesco Orabona · Marius Kloft · Andres Munoz · Gunnar Rätsch · Hal Daumé III · Mehryar Mohri · Xuezhi Wang · Daniel Hernández-lobato · Song Liu · Thomas Unterthiner · Pascal Germain · Vinay P Namboodiri · Michael Goetz · Christopher Berlind · Sigurd Spieckermann · Marta Soare · Yujia Li · Vitaly Kuznetsov · Wenzhao Lian · Daniele Calandriello · Emilie Morvant -
2014 Workshop: Discrete Optimization in Machine Learning »
Jeffrey A Bilmes · Andreas Krause · Stefanie Jegelka · S Thomas McCormick · Sebastian Nowozin · Yaron Singer · Dhruv Batra · Volkan Cevher -
2014 Workshop: 3rd NIPS Workshop on Probabilistic Programming »
Daniel Roy · Josh Tenenbaum · Thomas Dietterich · Stuart J Russell · YI WU · Ulrik R Beierholm · Alp Kucukelbir · Zenna Tavares · Yura Perov · Daniel Lee · Brian Ruttenberg · Sameer Singh · Michael Hughes · Marco Gaboardi · Alexey Radul · Vikash Mansinghka · Frank Wood · Sebastian Riedel · Prakash Panangaden -
2014 Workshop: From Bad Models to Good Policies (Sequential Decision Making under Uncertainty) »
Odalric-Ambrym Maillard · Timothy A Mann · Shie Mannor · Jeremie Mary · Laurent Orseau · Thomas Dietterich · Ronald Ortner · Peter Grünwald · Joelle Pineau · Raphael Fonteneau · Georgios Theocharous · Esteban D Arcaute · Christos Dimitrakakis · Nan Jiang · Doina Precup · Pierre-Luc Bacon · Marek Petrik · Aviv Tamar -
2014 Poster: Communication-Efficient Distributed Dual Coordinate Ascent »
Martin Jaggi · Virginia Smith · Martin Takac · Jonathan Terhorst · Sanjay Krishnan · Thomas Hofmann · Michael Jordan -
2014 Poster: Sparse PCA with Oracle Property »
Quanquan Gu · Zhaoran Wang · Han Liu -
2014 Poster: "How hard is my MDP?" The distribution-norm to the rescue »
Odalric-Ambrym Maillard · Timothy A Mann · Shie Mannor -
2014 Poster: Difference of Convex Functions Programming for Reinforcement Learning »
Bilal Piot · Matthieu Geist · Olivier Pietquin -
2014 Poster: Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing »
Yuchen Zhang · Xi Chen · Denny Zhou · Michael Jordan -
2014 Poster: Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics »
Sergey Levine · Pieter Abbeel -
2014 Poster: Constrained convex minimization via model-based excessive gap »
Quoc Tran-Dinh · Volkan Cevher -
2014 Poster: Parallel Double Greedy Submodular Maximization »
Xinghao Pan · Stefanie Jegelka · Joseph Gonzalez · Joseph K Bradley · Michael Jordan -
2014 Poster: Robust Logistic Regression and Classification »
Jiashi Feng · Huan Xu · Shie Mannor · Shuicheng Yan -
2014 Spotlight: Difference of Convex Functions Programming for Reinforcement Learning »
Bilal Piot · Matthieu Geist · Olivier Pietquin -
2014 Spotlight: Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics »
Sergey Levine · Pieter Abbeel -
2014 Oral: "How hard is my MDP?" The distribution-norm to the rescue »
Odalric-Ambrym Maillard · Timothy A Mann · Shie Mannor -
2014 Spotlight: Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing »
Yuchen Zhang · Xi Chen · Denny Zhou · Michael Jordan -
2014 Poster: Robust Tensor Decomposition with Gross Corruption »
Quanquan Gu · Huan Gui · Jiawei Han -
2014 Poster: On the Convergence Rate of Decomposable Submodular Function Minimization »
Robert Nishihara · Stefanie Jegelka · Michael Jordan -
2014 Poster: Time--Data Tradeoffs by Aggressive Smoothing »
John J Bruer · Joel A Tropp · Volkan Cevher · Stephen Becker -
2013 Workshop: New Directions in Transfer and Multi-Task: Learning Across Domains and Tasks »
Urun Dogan · Marius Kloft · Tatiana Tommasi · Francesco Orabona · Massimiliano Pontil · Sinno Jialin Pan · Shai Ben-David · Arthur Gretton · Fei Sha · Marco Signoretto · Rajhans Samdani · Yun-Qian Miao · Mohammad Gheshlaghi azar · Ruth Urner · Christoph Lampert · Jonathan How -
2013 Workshop: MLINI-13: Machine Learning and Interpretation in Neuroimaging (Day 2) »
Georg Langs · Brian Murphy · Kai-min K Chang · Paolo Avesani · James Haxby · Nikolaus Kriegeskorte · Susan Whitfield-Gabrieli · Irina Rish · Guillermo Cecchi · Raif Rustamov · Marius Kloft · Jonathan Young · Sina Ghiassian · Michael Coen -
2013 Workshop: Big Learning : Advances in Algorithms and Data Management »
Xinghao Pan · Haijie Gu · Joseph Gonzalez · Sameer Singh · Yucheng Low · Joseph Hellerstein · Derek G Murray · Raghu Ramakrishnan · Michael Jordan · Christopher Ré -
2013 Workshop: Discrete Optimization in Machine Learning: Connecting Theory and Practice »
Stefanie Jegelka · Andreas Krause · Pradeep Ravikumar · Kazuo Murota · Jeffrey A Bilmes · Yisong Yue · Michael Jordan -
2013 Workshop: MLINI-13: Machine Learning and Interpretation in Neuroimaging (Day 1) »
Georg Langs · Brian Murphy · Kai-min K Chang · Paolo Avesani · James Haxby · Nikolaus Kriegeskorte · Susan Whitfield-Gabrieli · Irina Rish · Guillermo Cecchi · Raif Rustamov · Marius Kloft · Jonathan Young · Sina Ghiassian · Michael Coen -
2013 Poster: Variational Policy Search via Trajectory Optimization »
Sergey Levine · Vladlen Koltun -
2013 Poster: Reinforcement Learning in Robust Markov Decision Processes »
Shiau Hong Lim · Huan Xu · Shie Mannor -
2013 Poster: High-Dimensional Gaussian Process Bandits »
Josip Djolonga · Andreas Krause · Volkan Cevher -
2013 Session: Oral Session 10 »
Michael Jordan -
2013 Poster: Online PCA for Contaminated Data »
Jiashi Feng · Huan Xu · Shie Mannor · Shuicheng Yan -
2013 Poster: A Comparative Framework for Preconditioned Lasso Algorithms »
Fabian L Wauthier · Nebojsa Jojic · Michael Jordan -
2013 Poster: From Bandits to Experts: A Tale of Domination and Independence »
Noga Alon · Nicolò Cesa-Bianchi · Claudio Gentile · Yishay Mansour -
2013 Poster: Learning Multiple Models via Regularized Weighting »
Daniel Vainsencher · Shie Mannor · Huan Xu -
2013 Poster: Information-theoretic lower bounds for distributed statistical estimation with communication constraints »
Yuchen Zhang · John Duchi · Michael Jordan · Martin J Wainwright -
2013 Poster: Learning Kernels Using Local Rademacher Complexity »
Corinna Cortes · Marius Kloft · Mehryar Mohri -
2013 Oral: From Bandits to Experts: A Tale of Domination and Independence »
Noga Alon · Nicolò Cesa-Bianchi · Claudio Gentile · Yishay Mansour -
2013 Oral: Information-theoretic lower bounds for distributed statistical estimation with communication constraints »
Yuchen Zhang · John Duchi · Michael Jordan · Martin J Wainwright -
2013 Spotlight: Learning Kernels Using Local Rademacher Complexity »
Corinna Cortes · Marius Kloft · Mehryar Mohri -
2013 Poster: Optimistic Concurrency Control for Distributed Unsupervised Learning »
Xinghao Pan · Joseph Gonzalez · Stefanie Jegelka · Tamara Broderick · Michael Jordan -
2013 Poster: Efficient Algorithm for Privately Releasing Smooth Queries »
Ziteng Wang · Kai Fan · Jiaqi Zhang · Liwei Wang -
2013 Poster: Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation »
John Duchi · Martin J Wainwright · Michael Jordan -
2013 Poster: Streaming Variational Bayes »
Tamara Broderick · Nicholas Boyd · Andre Wibisono · Ashia C Wilson · Michael Jordan -
2013 Poster: When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity »
Anima Anandkumar · Daniel Hsu · Majid Janzamin · Sham M Kakade -
2013 Poster: Estimation, Optimization, and Parallelism when Data is Sparse »
John Duchi · Michael Jordan · Brendan McMahan -
2012 Workshop: Bayesian Nonparametric Models For Reliable Planning And Decision-Making Under Uncertainty »
Jonathan How · Lawrence Carin · John Fisher III · Michael Jordan · Alborz Geramifard -
2012 Poster: Selective Labeling via Error Bound Minimization »
Quanquan Gu · Tong Zhang · Chris Ding · Jiawei Han -
2012 Poster: Inverse Reinforcement Learning through Structured Classification »
Edouard Klein · Matthieu Geist · BILAL PIOT · Olivier Pietquin -
2012 Poster: Privacy Aware Learning »
John Duchi · Michael Jordan · Martin J Wainwright -
2012 Poster: Learning Mixtures of Tree Graphical Models »
Anima Anandkumar · Daniel Hsu · Furong Huang · Sham M Kakade -
2012 Poster: A Spectral Algorithm for Latent Dirichlet Allocation »
Anima Anandkumar · Dean P Foster · Daniel Hsu · Sham M Kakade · Yi-Kai Liu -
2012 Poster: Ancestor Sampling for Particle Gibbs »
Fredrik Lindsten · Michael Jordan · Thomas Schön -
2012 Poster: Identifiability and Unmixing of Latent Parse Trees »
Percy Liang · Sham M Kakade · Daniel Hsu -
2012 Poster: Dimensionality Dependent PAC-Bayes Margin Bound »
Chi Jin · Liwei Wang -
2012 Spotlight: A Spectral Algorithm for Latent Dirichlet Allocation »
Anima Anandkumar · Dean P Foster · Daniel Hsu · Sham M Kakade · Yi-Kai Liu -
2012 Oral: Privacy Aware Learning »
John Duchi · Michael Jordan · Martin J Wainwright -
2012 Poster: Convex Multi-view Subspace Learning »
Martha White · Yao-Liang Yu · Xinhua Zhang · Dale Schuurmans -
2012 Poster: Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods »
John Duchi · Michael Jordan · Martin J Wainwright · Andre Wibisono -
2012 Poster: Active Learning of Multi-Index Function Models »
Hemant Tyagi · Volkan Cevher -
2012 Poster: The Perturbed Variation »
Maayan Harel · Shie Mannor -
2012 Poster: Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models »
Ke Jiang · Brian Kulis · Michael Jordan -
2011 Workshop: Big Learning: Algorithms, Systems, and Tools for Learning at Scale »
Joseph E Gonzalez · Sameer Singh · Graham Taylor · James Bergstra · Alice Zheng · Misha Bilenko · Yucheng Low · Yoshua Bengio · Michael Franklin · Carlos Guestrin · Andrew McCallum · Alexander Smola · Michael Jordan · Sugato Basu -
2011 Poster: Bayesian Bias Mitigation for Crowdsourcing »
Fabian L Wauthier · Michael Jordan -
2011 Poster: Divide-and-Conquer Matrix Factorization »
Lester W Mackey · Ameet S Talwalkar · Michael Jordan -
2011 Poster: From Bandits to Experts: On the Value of Side-Observations »
Shie Mannor · Ohad Shamir -
2011 Poster: Stochastic convex optimization with bandit feedback »
Alekh Agarwal · Dean P Foster · Daniel Hsu · Sham M Kakade · Sasha Rakhlin -
2011 Spotlight: From Bandits to Experts: On the Value of Side-Observations »
Shie Mannor · Ohad Shamir -
2011 Poster: An Empirical Evaluation of Thompson Sampling »
Olivier Chapelle · Lihong Li -
2011 Poster: Spectral Methods for Learning Multivariate Latent Tree Structure »
Anima Anandkumar · Kamalika Chaudhuri · Daniel Hsu · Sham M Kakade · Le Song · Tong Zhang -
2011 Poster: The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning »
Marius Kloft · Gilles Blanchard -
2011 Poster: Committing Bandits »
Loc X Bui · Ramesh Johari · Shie Mannor -
2011 Poster: Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression »
Sham M Kakade · Adam Kalai · Varun Kanade · Ohad Shamir -
2010 Workshop: New Directions in Multiple Kernel Learning »
Marius Kloft · Ulrich Rueckert · Cheng Soon Ong · Alain Rakotomamonjy · Soeren Sonnenburg · Francis Bach -
2010 Spotlight: Learning from Logged Implicit Exploration Data »
Alex Strehl · Lihong Li · John Langford · Sham M Kakade -
2010 Oral: Tree-Structured Stick Breaking for Hierarchical Data »
Ryan Adams · Zoubin Ghahramani · Michael Jordan -
2010 Invited Talk: Statistical Inference of Protein Structure and Function »
Michael Jordan -
2010 Spotlight: Online Classification with Specificity Constraints »
Andrey Bernstein · Shie Mannor · Nahum Shimkin -
2010 Poster: Tree-Structured Stick Breaking for Hierarchical Data »
Ryan Adams · Zoubin Ghahramani · Michael Jordan -
2010 Poster: Feature Construction for Inverse Reinforcement Learning »
Sergey Levine · Zoran Popovic · Vladlen Koltun -
2010 Poster: Online Classification with Specificity Constraints »
Andrey Bernstein · Shie Mannor · Nahum Shimkin -
2010 Poster: Distributionally Robust Markov Decision Processes »
Huan Xu · Shie Mannor -
2010 Poster: Learning from Logged Implicit Exploration Data »
Alexander L Strehl · John Langford · Lihong Li · Sham M Kakade -
2010 Spotlight: Variational Inference over Combinatorial Spaces »
Alexandre Bouchard-Côté · Michael Jordan -
2010 Poster: Variational Inference over Combinatorial Spaces »
Alexandre Bouchard-Côté · Michael Jordan -
2010 Poster: Unsupervised Kernel Dimension Reduction »
Meihong Wang · Fei Sha · Michael Jordan -
2010 Poster: Relaxed Clipping: A Global Training Method for Robust Regression and Classification »
Yao-Liang Yu · Min Yang · Linli Xu · Martha White · Dale Schuurmans -
2010 Poster: Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains »
Martha White · Adam M White -
2010 Poster: Heavy-Tailed Process Priors for Selective Shrinkage »
Fabian L Wauthier · Michael Jordan -
2010 Poster: Parallelized Stochastic Gradient Descent »
Martin A Zinkevich · Markus Weimer · Alexander Smola · Lihong Li -
2010 Poster: Random Conic Pursuit for Semidefinite Programming »
Ariel Kleiner · ali rahimi · Michael Jordan -
2009 Workshop: Nonparametric Bayes »
Dilan Gorur · Francois Caron · Yee Whye Teh · David B Dunson · Zoubin Ghahramani · Michael Jordan -
2009 Workshop: Manifolds, sparsity, and structured models: When can low-dimensional geometry really help? »
Richard Baraniuk · Volkan Cevher · Mark A Davenport · Piotr Indyk · Bruno Olshausen · Michael B Wakin -
2009 Poster: Sufficient Conditions for Agnostic Active Learnable »
Liwei Wang -
2009 Poster: Sharing Features among Dynamical Systems with Beta Processes »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2009 Oral: Sharing Features among Dynamical Systems with Beta Processes »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2009 Poster: Multi-Label Prediction via Compressed Sensing »
Daniel Hsu · Sham M Kakade · John Langford · Tong Zhang -
2009 Poster: Efficient and Accurate Lp-Norm Multiple Kernel Learning »
Marius Kloft · Ulf Brefeld · Soeren Sonnenburg · Pavel Laskov · Klaus-Robert Müller · Alexander Zien -
2009 Oral: Multi-Label Prediction via Compressed Sensing »
Daniel Hsu · Sham M Kakade · John Langford · Tong Zhang -
2009 Poster: Asymptotically Optimal Regularization in Smooth Parametric Models »
Percy Liang · Francis Bach · Guillaume Bouchard · Michael Jordan -
2009 Poster: Nonparametric Latent Feature Models for Link Prediction »
Kurt T Miller · Tom Griffiths · Michael Jordan -
2009 Poster: Learning with Compressible Priors »
Volkan Cevher -
2009 Spotlight: Nonparametric Latent Feature Models for Link Prediction »
Kurt T Miller · Tom Griffiths · Michael Jordan -
2008 Oral: Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes »
Erik Sudderth · Michael Jordan -
2008 Poster: Nonparametric Bayesian Learning of Switching Linear Dynamical Systems »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2008 Poster: High-dimensional union support recovery in multivariate regression »
Guillaume R Obozinski · Martin J Wainwright · Michael Jordan -
2008 Poster: Sparse Signal Recovery Using Markov Random Fields »
Volkan Cevher · Marco F Duarte · Chinmay Hegde · Richard Baraniuk -
2008 Poster: Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes »
Erik Sudderth · Michael Jordan -
2008 Spotlight: Sparse Signal Recovery Using Markov Random Fields »
Volkan Cevher · Marco F Duarte · Chinmay Hegde · Richard Baraniuk -
2008 Spotlight: High-dimensional union support recovery in multivariate regression »
Guillaume R Obozinski · Martin J Wainwright · Michael Jordan -
2008 Spotlight: Nonparametric Bayesian Learning of Switching Linear Dynamical Systems »
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky -
2008 Poster: Mind the Duality Gap: Logarithmic regret algorithms for online optimization »
Shai Shalev-Shwartz · Sham M Kakade -
2008 Poster: Sparse Online Learning via Truncated Gradient »
John Langford · Lihong Li · Tong Zhang -
2008 Poster: On the Generalization Ability of Online Strongly Convex Programming Algorithms »
Sham M Kakade · Ambuj Tewari -
2008 Poster: Posterior Consistency of the Silverman g-prior in Bayesian Model Choice »
Zhihua Zhang · Michael Jordan · Dit-Yan Yeung -
2008 Poster: DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification »
Simon Lacoste-Julien · Fei Sha · Michael Jordan -
2008 Spotlight: Sparse Online Learning via Truncated Gradient »
John Langford · Lihong Li · Tong Zhang -
2008 Spotlight: On the Generalization Ability of Online Strongly Convex Programming Algorithms »
Sham M Kakade · Ambuj Tewari -
2008 Spotlight: Posterior Consistency of the Silverman g-prior in Bayesian Model Choice »
Zhihua Zhang · Michael Jordan · Dit-Yan Yeung -
2008 Spotlight: Mind the Duality Gap: Logarithmic regret algorithms for online optimization »
Shai Shalev-Shwartz · Sham M Kakade -
2008 Poster: Efficient Inference in Phylogenetic InDel Trees »
Alexandre Bouchard-Côté · Michael Jordan · Dan Klein -
2008 Poster: Spectral Clustering with Perturbed Data »
Ling Huang · Donghui Yan · Michael Jordan · Nina Taft -
2008 Poster: On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization »
Sham M Kakade · Karthik Sridharan · Ambuj Tewari -
2008 Poster: Bounding Performance Loss in Approximate MDP Homomorphisms »
Doina Precup · Jonathan Taylor Taylor · Prakash Panangaden -
2008 Spotlight: Efficient Inference in Phylogenetic InDel Trees »
Alexandre Bouchard-Côté · Michael Jordan · Dan Klein -
2008 Spotlight: Spectral Clustering with Perturbed Data »
Ling Huang · Donghui Yan · Michael Jordan · Nina Taft -
2007 Poster: Agreement-Based Learning »
Percy Liang · Dan Klein · Michael Jordan -
2007 Spotlight: Agreement-Based Learning »
Percy Liang · Dan Klein · Michael Jordan -
2007 Oral: The Price of Bandit Information for Online Optimization »
Varsha Dani · Thomas P Hayes · Sham M Kakade -
2007 Poster: The Price of Bandit Information for Online Optimization »
Varsha Dani · Thomas P Hayes · Sham M Kakade -
2007 Spotlight: Resampling Methods for Protein Structure Prediction with Rosetta »
Ben Blum · David Baker · Michael Jordan · Philip Bradley · Rhiju Das · David Kim -
2007 Spotlight: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization »
XuanLong Nguyen · Martin J Wainwright · Michael Jordan -
2007 Poster: Resampling Methods for Protein Structure Prediction with Rosetta »
Ben Blum · David Baker · Michael Jordan · Philip Bradley · Rhiju Das · David Kim -
2007 Poster: Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization »
XuanLong Nguyen · Martin J Wainwright · Michael Jordan -
2006 Poster: Distributed PCA and Network Anomaly Detection »
Ling Huang · XuanLong Nguyen · Minos Garofalakis · Michael Jordan · Anthony D Joseph · Nina Taft