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Author Information
Aaron Sidford (Stanford)
Aditya Mahajan (McGill University)
Alejandro Ribeiro (University of Pennsylvania)
Alex Lewandowski (University of Alberta)
Ali H Sayed (Ecole Polytechnique Fédérale de Lausanne)
A. H. Sayed is Dean of Engineering at EPFL, Switzerland, and principal investigator of the Adaptive Systems Laboratory. He has served as distinguished professor and chairman of electrical engineering at UCLA. An author/co-author of over 530 scholarly publications and six books, his research involves several areas including adaptation and learning theories, data and network sciences, statistical inference, and distributed optimization. He is recognized as a Highly Cited Researcher by Thomson Reuters and Clarivate Analytics, and is a member of the US National Academy of Engineering. He is serving as President of the IEEE Signal Processing Society.
Ambuj Tewari (University of Michigan)
Angelika Steger (ETH Zurich)
Anima Anandkumar (NVIDIA / Caltech)
Anima Anandkumar is a Bren professor at Caltech CMS department and a director of machine learning research at NVIDIA. Her research spans both theoretical and practical aspects of large-scale machine learning. In particular, she has spearheaded research in tensor-algebraic methods, non-convex optimization, probabilistic models and deep learning. Anima is the recipient of several awards and honors such as the Bren named chair professorship at Caltech, Alfred. P. Sloan Fellowship, Young investigator awards from the Air Force and Army research offices, Faculty fellowships from Microsoft, Google and Adobe, and several best paper awards. Anima received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, a visiting researcher at Microsoft Research New England in 2012 and 2014, an assistant professor at U.C. Irvine between 2010 and 2016, an associate professor at U.C. Irvine between 2016 and 2017 and a principal scientist at Amazon Web Services between 2016 and 2018.
Asier Mujika (ETH Zurich)
Hilbert J Kappen (Radboud University)
Bolei Zhou (CUHK)
Byron Boots (Georgia Tech / Google Brain)
Chelsea Finn (Stanford University)
Chen-Yu Wei (University of Southern California)
Chi Jin (UC Berkeley)
Ching-An Cheng (Georgia Tech)
Christina Yu (Cornell University)
Clement Gehring (Massachusetts Institute of Technology)
Craig Boutilier (Google)
Dahua Lin (The Chinese University of Hong Kong)
Daniel McNamee (University College London)
Daniel Russo (Columbia University)
David Brandfonbrener (New York University)
Denny Zhou (Google)
Devesh Jha (MERL)
Diego Romeres (Mitsubishi Electric Research Laboratories)
I am a Principal Research Scientist at MERL, mainly working in machine learning applied to robotics. Main research areas are robotic manipulation, probabilistic models, reinforcement learning.
Doina Precup (McGill University / Mila / DeepMind Montreal)
Dominik Thalmeier (Radboud University)
Eduard Gorbunov (Moscow Institute of Physics and Technology)
Elad Hazan (Princeton University)
Elena Smirnova (Criteo)
Elvis Dohmatob (Criteo)
Emma Brunskill (Stanford University)
Enrique Munoz de Cote (Prowler.io)
Ethan Waldie (University of Toronto & Palantir Technologies)
Florian Meier (ETH Zurich)
Florian Schaefer (Caltech)
Ge Liu (MIT)
Gergely Neu (Universitat Pompeu Fabra)
Haim Kaplan (TAU, GOOGLE)
Hao Sun (CUHK)
Hengshuai Yao (Huawei Technologies)
I studied reinforcement learning at Reinforcement Learning and Artificial Intelligence (RLAI) lab from 2008 to 2014 in a Ph.D program at Department of Computing Science, University of Alberta. My thesis is on model-based reinforcement learning with linear function approximation. During my Ph.D studies, I worked with Csaba Szepesvari, Rich Sutton, Dale Schuurmans, and Davood Rafiei on reinforcement learning theory, algorithms and web applications. I joined NCSoft game studio in San Francisco in 2016 working on reinforcement learning in games. I moved back to Canada and joined Huawei in 2017.
Jalaj Bhandari (Columbia University)
I am a PhD student in the IEOR Department at Columbia University, currently working on design and analysis of Reinforcement Learning (RL) algorithms with Prof. Garud Iyengar and Prof. Daniel Russo. In the past, I have done research work with Prof. John P. Cunningham on Bayesian Machine learning, specifically in designing computationally efficient Markov Chain Monte Carlo (MCMC) algorithms for posterior sampling. I broadly interested in working at the intersection of Optimization and Machine Learning.
James A Preiss (University of Southern California)
Jayakumar Subramanian (McGill University)
Jiajin Li (The Chinese University of Hong Kong)
Jieping Ye (University of Michigan)
Jimmy Smith (Stanford)
Joan Bas Serrano (Universitat Pompeu Fabra)
Joan Bruna (NYU)
John Langford (Microsoft Research New York)
Jonathan Lee (UC Berkeley)
Jose A. Arjona-Medina (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria)
Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC))
Karan Singh (Princeton University)
Yuping Luo (Princeton University)
Zafarali Ahmed (McGill University)
Zaiwei Chen (Georgia Institute of Technology)
Zhaoran Wang (Northwestern University)
Zhizhong Li (The Chinese University of Hong Kong)
Zhuoran Yang (Princeton University)
Ziping Xu (University of Michigan)
My name is Ziping Xu. I am a fifth-year Ph.D. student in Statistics at the University of Michigan. My research interests are on sample efficient reinforcement learning and transfer learning, multitask learning. I am looking for research-orientated full-time job starting Fall 2023
Ziyang Tang (UT Austin)
Yi Mao (Microsoft)
David Brandfonbrener (New York University)
Shirli Di-Castro (Technion)
Riashat Islam (MILA/McGill)
Zuyue Fu (Northwestern University)
Abhishek Naik (University of Alberta)
Saurabh Kumar (Stanford University)
Benjamin Petit (Stanford University)
Angeliki Kamoutsi (ETH Zurich)
Simone Totaro (Universitat Pompeu Fabra)
Arvind Raghunathan (MERL)
Arvind's research focuses on algorithms for optimization of large-scale nonlinear and mixed integer nonlinear programs with applications in power grid, transportation systems and model-based control of processes. He previously worked at the United Technologies Research Center for 7 years developing optimization algorithms for aerospace, elevator, energy systems and security businesses.
Rui Wu (Google)
Donghwan Lee (KAIST)
Dongsheng Ding (University of Southern California)
Alec Koppel (U.S. Army Research Laboratory)
Hao Sun (Peng Cheng Laboratory)
Christian Tjandraatmadja (Google)
Mahdi Karami (University of Alberta)
Jincheng Mei (University of Alberta)
Chenjun Xiao (University of Alberta)
Junfeng Wen (University of Alberta)
Zichen Zhang (University of Alberta)
Ross Goroshin (Google Brain)
Mohammad Pezeshki (Mila)
Jiaqi Zhai (Google)
Philip Amortila (University of Illinois at Urbana-Champaign)
Shuo Huang (Georgia Institute of Technology)
Mariya Vasileva (University of Illinois at Urbana-Champaign)
El houcine Bergou (KAUST-INRA)
Adel Ahmadyan (Google)
Haoran Sun (Georgia Institute of Technology)
Sheng Zhang (Georgia Institute of Technology)
I am currently a final-year PhD student in Machine Learning Program at Georgia Tech. I am fortunate to be advised by Prof. Justin Romberg and Prof. Ashwin Pananjady. Before coming to Georgia Tech, I graduated with an MS in Applied Mathematics from Columbia University and a BS in Mathematics and Applied Mathematics from Wuhan University. My research mainly focuses on reinforcement learning (RL) and distributed optimization. The overall goal of my research is to enhance the theoretical understanding of RL, and to design efficient algorithms for large-scale problems arise from machine-learning and decision-making applications. Specifically, I have studied the statistical efficiency (sample complexity) of RL algorithms, and designed an accelerated method for distributed stochastic optimization problems. In addition, during my previous research internships, I have developed an AI program for a popular Chinese poker game using self-play deep RL, proposed a matrix factorization framework for high-dimensional demand forecasting with missing values, and designed deep convolutional neural networks for automated image segmentation of neurons.
Lukas Gruber (Johannes Kepler University)
Yuanhao Wang (Tsinghua University)
Tetiana Parshakova (Stanford U)
I am now pursuing a Ph.D. in Computational & Mathematical Engineering at Stanford University. I received a Bachelors in the Department of Industrial Design at KAIST. Then, I obtained a Master’s degree at KAIST School of Electrical Engineering.
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Kaiyu Yang · Aidan Swope · Alex Gu · Rahul Chalamala · Peiyang Song · Shixing Yu · Saad Godil · Ryan J Prenger · Animashree Anandkumar -
2023 Oral: ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate models »
Sungduk Yu · Walter Hannah · Liran Peng · Jerry Lin · Mohamed Aziz Bhouri · Ritwik Gupta · Björn Lütjens · Justus C. Will · Gunnar Behrens · Nora Loose · Charles Stern · Tom Beucler · Bryce Harrop · Benjamin Hillman · Andrea Jenney · Savannah L. Ferretti · Nana Liu · Animashree Anandkumar · Noah Brenowitz · Veronika Eyring · Nicholas Geneva · Pierre Gentine · Stephan Mandt · Jaideep Pathak · Akshay Subramaniam · Carl Vondrick · Rose Yu · Laure Zanna · Ryan Abernathey · Fiaz Ahmed · David Bader · Pierre Baldi · Elizabeth Barnes · Christopher Bretherton · Julius Busecke · Peter Caldwell · Wayne Chuang · Yilun Han · YU HUANG · Fernando Iglesias-Suarez · Sanket Jantre · Karthik Kashinath · Marat Khairoutdinov · Thorsten Kurth · Nicholas Lutsko · Po-Lun Ma · Griffin Mooers · J. David Neelin · David Randall · Sara Shamekh · Mark Taylor · Nathan Urban · Janni Yuval · Guang Zhang · Tian Zheng · Mike Pritchard -
2023 Workshop: Workshop on Advancing Neural Network Training (WANT): Computational Efficiency, Scalability, and Resource Optimization »
Julia Gusak · Jean Kossaifi · Alena Shilova · Cristiana Bentes · Animashree Anandkumar · Olivier Beaumont -
2023 Workshop: The Symbiosis of Deep Learning and Differential Equations -- III »
Luca Celotti · Martin Magill · Ermal Rrapaj · Winnie Xu · Qiyao Wei · Archis Joglekar · Michael Poli · Animashree Anandkumar -
2023 Workshop: New Frontiers in Graph Learning (GLFrontiers) »
Jiaxuan You · Rex Ying · Hanjun Dai · Ge Liu · Azalia Mirhoseini · Smita Krishnaswamy -
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 -
2023 Workshop: OPT 2023: Optimization for Machine Learning »
Cristóbal Guzmán · Courtney Paquette · Katya Scheinberg · Aaron Sidford · Sebastian Stich -
2022 : Contributed Talk: DensePure: Understanding Diffusion Models towards Adversarial Robustness »
Zhongzhu Chen · Kun Jin · Jiongxiao Wang · Weili Nie · Mingyan Liu · Anima Anandkumar · Bo Li · Dawn Song -
2022 : Factor Investing with a Deep Multi-Factor Model »
Zikai Wei · Bo Dai · Dahua Lin -
2022 Workshop: Trustworthy and Socially Responsible Machine Learning »
Huan Zhang · Linyi Li · Chaowei Xiao · J. Zico Kolter · Anima Anandkumar · Bo Li -
2022 Spotlight: Lightning Talks 4B-4 »
Ziyue Jiang · Zeeshan Khan · Yuxiang Yang · Chenze Shao · Yichong Leng · Zehao Yu · Wenguan Wang · Xian Liu · Zehua Chen · Yang Feng · Qianyi Wu · James Liang · C.V. Jawahar · Junjie Yang · Zhe Su · Songyou Peng · Yufei Xu · Junliang Guo · Michael Niemeyer · Hang Zhou · Zhou Zhao · Makarand Tapaswi · Dongfang Liu · Qian Yang · Torsten Sattler · Yuanqi Du · Haohe Liu · Jing Zhang · Andreas Geiger · Yi Ren · Long Lan · Jiawei Chen · Wayne Wu · Dahua Lin · Dacheng Tao · Xu Tan · Jinglin Liu · Ziwei Liu · 振辉 叶 · Danilo Mandic · Lei He · Xiangyang Li · Tao Qin · sheng zhao · Tie-Yan Liu -
2022 Spotlight: Audio-Driven Co-Speech Gesture Video Generation »
Xian Liu · Qianyi Wu · Hang Zhou · Yuanqi Du · Wayne Wu · Dahua Lin · Ziwei 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 : Aaron Sidford, Efficiently Minimizing the Maximum Loss »
Aaron Sidford -
2022 : Simulating Human Gaze with Neural Visual Attention »
Leo Schwinn · Doina Precup · Bjoern Eskofier · Dario Zanca -
2022 : HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression »
Jiaqi Gu · Ben Keller · Jean Kossaifi · Anima Anandkumar · Brucek Khailany · David Pan -
2022 : Simulating Human Gaze with Neural Visual Attention »
Leo Schwinn · Doina Precup · Bjoern Eskofier · Dario Zanca -
2022 : Dynamic-backbone protein-ligand structure prediction with multiscale generative diffusion models »
Zhuoran Qiao · Weili Nie · Arash Vahdat · Thomas Miller · Anima Anandkumar -
2022 : Poster Session 1 »
Andrew Lowy · Thomas Bonnier · Yiling Xie · Guy Kornowski · Simon Schug · Seungyub Han · Nicolas Loizou · xinwei zhang · Laurent Condat · Tabea E. Röber · Si Yi Meng · Marco Mondelli · Runlong Zhou · Eshaan Nichani · Adrian Goldwaser · Rudrajit Das · Kayhan Behdin · Atish Agarwala · Mukul Gagrani · Gary Cheng · Tian Li · Haoran Sun · Hossein Taheri · Allen Liu · Siqi Zhang · Dmitrii Avdiukhin · Bradley Brown · Miaolan Xie · Junhyung Lyle Kim · Sharan Vaswani · Xinmeng Huang · Ganesh Ramachandra Kini · Angela Yuan · Weiqiang Zheng · Jiajin Li -
2022 : John Langford »
John Langford -
2022 Workshop: Machine Learning and the Physical Sciences »
Atilim Gunes Baydin · Adji Bousso Dieng · Emine Kucukbenli · Gilles Louppe · Siddharth Mishra-Sharma · Benjamin Nachman · Brian Nord · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Lenka Zdeborová · Rianne van den Berg -
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 : Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Design »
Mayleen Cortez · Matthew Eichhorn · Christina Yu -
2022 : HandsOff: Labeled Dataset Generation with No Additional Human Annotations »
Austin Xu · Mariya Vasileva · Arjun Seshadri -
2022 Workshop: 3rd Offline Reinforcement Learning Workshop: Offline RL as a "Launchpad" »
Aviral Kumar · Rishabh Agarwal · Aravind Rajeswaran · Wenxuan Zhou · George Tucker · Doina Precup · Aviral Kumar -
2022 Workshop: AI for Science: Progress and Promises »
Yi Ding · Yuanqi Du · Tianfan Fu · Hanchen Wang · Anima Anandkumar · Yoshua Bengio · Anthony Gitter · Carla Gomes · Aviv Regev · Max Welling · Marinka Zitnik -
2022 Poster: A Simple Decentralized Cross-Entropy Method »
Zichen Zhang · Jun Jin · Martin Jagersand · Jun Luo · Dale Schuurmans -
2022 Poster: Oracle Inequalities for Model Selection in Offline Reinforcement Learning »
Jonathan N Lee · George Tucker · Ofir Nachum · Bo Dai · Emma Brunskill -
2022 Poster: Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge »
Mayleen Cortez · Matthew Eichhorn · Christina Yu -
2022 Poster: Exponential Separations in Symmetric Neural Networks »
Aaron Zweig · Joan Bruna -
2022 Poster: When does return-conditioned supervised learning work for offline reinforcement learning? »
David Brandfonbrener · Alberto Bietti · Jacob Buckman · Romain Laroche · Joan Bruna -
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: Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models »
Manli Shu · Weili Nie · De-An Huang · Zhiding Yu · Tom Goldstein · Anima Anandkumar · Chaowei Xiao -
2022 Poster: Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits »
Gergely Neu · Iuliia Olkhovskaia · Matteo Papini · Ludovic Schwartz -
2022 Poster: A Boosting Approach to Reinforcement Learning »
Nataly Brukhim · Elad Hazan · Karan Singh -
2022 Poster: Optimal Scaling for Locally Balanced Proposals in Discrete Spaces »
Haoran Sun · Hanjun Dai · Dale Schuurmans -
2022 Poster: Optimal and Adaptive Monteiro-Svaiter Acceleration »
Yair Carmon · Danielle Hausler · Arun Jambulapati · Yujia Jin · Aaron Sidford -
2022 Poster: Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits »
Tong Mu · Yash Chandak · Tatsunori Hashimoto · Emma Brunskill -
2022 Poster: Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropagation »
Ziyu Jiang · Xuxi Chen · Xueqin Huang · Xianzhi Du · Denny Zhou · Zhangyang Wang -
2022 Poster: PeRFception: Perception using Radiance Fields »
Yoonwoo Jeong · Seungjoo Shin · Junha Lee · Chris Choy · Anima Anandkumar · Minsu Cho · Jaesik Park -
2022 Poster: The Role of Baselines in Policy Gradient Optimization »
Jincheng Mei · Wesley Chung · Valentin Thomas · Bo Dai · Csaba Szepesvari · Dale Schuurmans -
2022 Poster: Proximal Point Imitation Learning »
Luca Viano · Angeliki Kamoutsi · Gergely Neu · Igor Krawczuk · Volkan Cevher -
2022 Poster: A Lagrangian Duality Approach to Active Learning »
Juan Elenter · Navid Naderializadeh · Alejandro Ribeiro -
2022 Poster: On Non-Linear operators for Geometric Deep Learning »
Grégoire Sergeant-Perthuis · Jakob Maier · Joan Bruna · Edouard Oyallon -
2022 Poster: Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant »
Ying Jin · Jiaqi Wang · Dahua Lin -
2022 Poster: Exploit Reward Shifting in Value-Based Deep-RL: Optimistic Curiosity-Based Exploration and Conservative Exploitation via Linear Reward Shaping »
Hao Sun · Lei Han · Rui Yang · Xiaoteng Ma · Jian Guo · Bolei Zhou -
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: Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits »
Tianyuan Jin · Pan Xu · Xiaokui Xiao · Anima Anandkumar -
2022 Poster: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models »
Jason Wei · Xuezhi Wang · Dale Schuurmans · Maarten Bosma · brian ichter · Fei Xia · Ed Chi · Quoc V Le · Denny Zhou -
2022 Poster: Off-Policy Evaluation for Action-Dependent Non-stationary Environments »
Yash Chandak · Shiv Shankar · Nathaniel Bastian · Bruno da Silva · Emma Brunskill · Philip Thomas -
2022 Poster: coVariance Neural Networks »
Saurabh Sihag · Gonzalo Mateos · Corey McMillan · Alejandro Ribeiro -
2022 Poster: Learning Chaotic Dynamics in Dissipative Systems »
Zongyi Li · Miguel Liu-Schiaffini · Nikola Kovachki · Kamyar Azizzadenesheli · Burigede Liu · Kaushik Bhattacharya · Andrew Stuart · Anima Anandkumar -
2022 Poster: Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models »
Boxin Wang · Wei Ping · Chaowei Xiao · Peng Xu · Mostofa Patwary · Mohammad Shoeybi · Bo Li · Anima Anandkumar · Bryan Catanzaro -
2022 Poster: Temporally-Consistent Survival Analysis »
Lucas Maystre · Daniel Russo -
2022 Poster: Adaptive Sampling for Discovery »
Ziping Xu · Eunjae Shim · Ambuj Tewari · Paul Zimmerman -
2022 Poster: Pre-Trained Language Models for Interactive Decision-Making »
Shuang Li · Xavier Puig · Chris Paxton · Yilun Du · Clinton Wang · Linxi Fan · Tao Chen · De-An Huang · Ekin Akyürek · Anima Anandkumar · Jacob Andreas · Igor Mordatch · Antonio Torralba · Yuke Zhu -
2022 Poster: Online Agnostic Multiclass Boosting »
Vinod Raman · Ambuj Tewari -
2022 Poster: MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge »
Linxi Fan · Guanzhi Wang · Yunfan Jiang · Ajay Mandlekar · Yuncong Yang · Haoyi Zhu · Andrew Tang · De-An Huang · Yuke Zhu · Anima Anandkumar -
2022 Poster: Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data »
Allen Nie · Yannis Flet-Berliac · Deon Jordan · William Steenbergen · Emma Brunskill -
2022 Poster: Continuous MDP Homomorphisms and Homomorphic Policy Gradient »
Sahand Rezaei-Shoshtari · Rosie Zhao · Prakash Panangaden · David Meger · Doina Precup -
2022 Poster: Giving Feedback on Interactive Student Programs with Meta-Exploration »
Evan Liu · Moritz Stephan · Allen Nie · Chris Piech · Emma Brunskill · Chelsea Finn -
2022 Poster: On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games »
Runyu Zhang · Jincheng Mei · Bo Dai · Dale Schuurmans · Na Li -
2022 Poster: Learning single-index models with shallow neural networks »
Alberto Bietti · Joan Bruna · Clayton Sanford · Min Jae Song -
2022 Poster: On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood »
Moses Charikar · Zhihao Jiang · Kirankumar Shiragur · Aaron Sidford -
2022 Poster: Open-Ended Reinforcement Learning with Neural Reward Functions »
Robert Meier · Asier Mujika -
2021 : Anima Anandkumar »
Anima Anandkumar -
2021 : Retrospective Panel »
Sergey Levine · Nando de Freitas · Emma Brunskill · Finale Doshi-Velez · Nan Jiang · Rishabh Agarwal -
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 : General Discussion 2 - What does the OOD problem mean to you and your field? with Anima Anandkumar, Terry Sejnowski, Chris White: General Discussion 2 »
Anima Anandkumar · Terry Sejnowski · Weiwei Yang · Joshua T Vogelstein -
2021 : Anima Anandkumar: Role of AI in predicting and mitigating climate change »
Anima Anandkumar -
2021 Workshop: Offline Reinforcement Learning »
Rishabh Agarwal · Aviral Kumar · George Tucker · Justin Fu · Nan Jiang · Doina Precup · Aviral Kumar -
2021 : Reward and State Design: Towards Learning to Teach »
Alex Lewandowski · Calarina Muslimani · Matthew Taylor · Jun Luo -
2021 : Efficient Quantum Optimization via Multi-Basis Encodings and Tensor Rings »
Anima Anandkumar -
2021 : Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update »
Jiawei Zhao · Steve Dai · Rangha Venkatesan · Brian Zimmer · Mustafa Ali · Ming-Yu Liu · Brucek Khailany · · Anima Anandkumar -
2021 : Safe RL Debate »
Sylvia Herbert · Animesh Garg · Emma Brunskill · Aleksandra Faust · Dylan Hadfield-Menell -
2021 : On Adaptivity and Confounding in Contextual Bandit Experiments »
Chao Qin · Daniel Russo -
2021 : Accelerating Systems and ML for Science »
Anima Anandkumar -
2021 Workshop: Machine Learning and the Physical Sciences »
Anima Anandkumar · Kyle Cranmer · Mr. Prabhat · Lenka Zdeborová · Atilim Gunes Baydin · Juan Carrasquilla · Emine Kucukbenli · Gilles Louppe · Benjamin Nachman · Brian Nord · Savannah Thais -
2021 Affinity Workshop: WiML Workshop 4 »
Soomin Aga Lee · Meera Desai · Nezihe Merve Gürel · Boyi Li · Linh Tran · Akiko Eriguchi · Jieyu Zhao · Salomey Osei · Sirisha Rambhatla · Geeticka Chauhan · Nwamaka (Amaka) Okafor · Mariya Vasileva -
2021 Poster: Play to Grade: Testing Coding Games as Classifying Markov Decision Process »
Allen Nie · Emma Brunskill · Chris Piech -
2021 Poster: Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes »
HyunJi Alex Nam · Scott Fleming · Emma Brunskill -
2021 Poster: Bellman-consistent Pessimism for Offline Reinforcement Learning »
Tengyang Xie · Ching-An Cheng · Nan Jiang · Paul Mineiro · Alekh Agarwal -
2021 Affinity Workshop: WiML Workshop 3 »
Soomin Aga Lee · Meera Desai · Nezihe Merve Gürel · Boyi Li · Linh Tran · Akiko Eriguchi · Jieyu Zhao · Salomey Osei · Sirisha Rambhatla · Geeticka Chauhan · Nwamaka (Amaka) Okafor · Mariya Vasileva -
2021 Affinity Workshop: WiML Workshop 2 »
Soomin Aga Lee · Meera Desai · Nezihe Merve Gürel · Boyi Li · Linh Tran · Akiko Eriguchi · Jieyu Zhao · Salomey Osei · Sirisha Rambhatla · Geeticka Chauhan · Nwamaka (Amaka) Okafor · Mariya Vasileva -
2021 Poster: Heuristic-Guided Reinforcement Learning »
Ching-An Cheng · Andrey Kolobov · Adith Swaminathan -
2021 Poster: Deconvolutional Networks on Graph Data »
Jia Li · Jiajin Li · Yang Liu · Jianwei Yu · Yueting Li · Hong Cheng -
2021 Poster: Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators »
Zaiwei Chen · Siva Theja Maguluri · Sanjay Shakkottai · Karthikeyan Shanmugam -
2021 Poster: Gradient Starvation: A Learning Proclivity in Neural Networks »
Mohammad Pezeshki · Oumar Kaba · Yoshua Bengio · Aaron Courville · Doina Precup · Guillaume Lajoie -
2021 Poster: Average-Reward Learning and Planning with Options »
Yi Wan · Abhishek Naik · Rich Sutton -
2021 Poster: Online Control of Unknown Time-Varying Dynamical Systems »
Edgar Minasyan · Paula Gradu · Max Simchowitz · Elad Hazan -
2021 Poster: On the Sample Complexity of Learning under Geometric Stability »
Alberto Bietti · Luca Venturi · Joan Bruna -
2021 Poster: Stochastic Bias-Reduced Gradient Methods »
Hilal Asi · Yair Carmon · Arun Jambulapati · Yujia Jin · Aaron Sidford -
2021 Poster: Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization »
Clement Gehring · Kenji Kawaguchi · Jiaoyang Huang · Leslie Kaelbling -
2021 Poster: Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning »
Andrea Zanette · Martin J Wainwright · Emma Brunskill -
2021 Poster: Multiclass Boosting and the Cost of Weak Learning »
Nataly Brukhim · Elad Hazan · Shay Moran · Indraneel Mukherjee · Robert Schapire -
2021 Poster: Representation Learning Beyond Linear Prediction Functions »
Ziping Xu · Ambuj Tewari -
2021 Poster: Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems »
Jimmy Smith · Scott Linderman · David Sussillo -
2021 Poster: Decentralized Q-learning in Zero-sum Markov Games »
Muhammed Sayin · Kaiqing Zhang · David Leslie · Tamer Basar · Asuman Ozdaglar -
2021 Poster: Causal Bandits with Unknown Graph Structure »
Yangyi Lu · Amirhossein Meisami · Ambuj Tewari -
2021 Poster: Universal Off-Policy Evaluation »
Yash Chandak · Scott Niekum · Bruno da Silva · Erik Learned-Miller · Emma Brunskill · Philip Thomas -
2021 Poster: Sim and Real: Better Together »
Shirli Di-Castro · Dotan Di Castro · Shie Mannor -
2021 : WiML Opening remarks »
Boyi Li · Mariya Vasileva -
2021 Affinity Workshop: WiML Workshop 1 »
Soomin Aga Lee · Meera Desai · Nezihe Merve Gürel · Boyi Li · Linh Tran · Akiko Eriguchi · Jieyu Zhao · Salomey Osei · Sirisha Rambhatla · Geeticka Chauhan · Nwamaka (Amaka) Okafor · Mariya Vasileva -
2021 Poster: Finite Sample Analysis of Average-Reward TD Learning and $Q$-Learning »
Sheng Zhang · Zhe Zhang · Siva Theja Maguluri -
2021 Poster: Multi-task Learning of Order-Consistent Causal Graphs »
Xinshi Chen · Haoran Sun · Caleb Ellington · Eric Xing · Le Song -
2021 Poster: Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games »
Yu Bai · Chi Jin · Huan Wang · Caiming Xiong -
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 Oral: Bellman-consistent Pessimism for Offline Reinforcement Learning »
Tengyang Xie · Ching-An Cheng · Nan Jiang · Paul Mineiro · Alekh Agarwal -
2021 Poster: Generative Occupancy Fields for 3D Surface-Aware Image Synthesis »
Xudong XU · Xingang Pan · Dahua Lin · Bo Dai -
2021 Poster: Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion »
Tong Wu · Liang Pan · Junzhe Zhang · Tai WANG · Ziwei Liu · Dahua Lin -
2021 Poster: Few-Shot Object Detection via Association and DIscrimination »
Yuhang Cao · Jiaqi Wang · Ying Jin · Tong Wu · Kai Chen · Ziwei Liu · Dahua Lin -
2021 Poster: Understanding the Effect of Stochasticity in Policy Optimization »
Jincheng Mei · Bo Dai · Chenjun Xiao · Csaba Szepesvari · Dale Schuurmans -
2021 Poster: Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms »
Chi Jin · Qinghua Liu · Sobhan Miryoosefi -
2021 Poster: On the Cryptographic Hardness of Learning Single Periodic Neurons »
Min Jae Song · Ilias Zadik · Joan Bruna -
2021 Poster: Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses »
Haipeng Luo · Chen-Yu Wei · Chung-Wei Lee -
2021 Poster: Modified Frank Wolfe in Probability Space »
Carson Kent · Jiajin Li · Jose Blanchet · Peter W Glynn -
2021 Poster: Temporally Abstract Partial Models »
Khimya Khetarpal · Zafarali Ahmed · Gheorghe Comanici · Doina Precup -
2021 Poster: Design of Experiments for Stochastic Contextual Linear Bandits »
Andrea Zanette · Kefan Dong · Jonathan N Lee · Emma Brunskill -
2021 Poster: Adversarial Robustness with Semi-Infinite Constrained Learning »
Alexander Robey · Luiz Chamon · George J. Pappas · Hamed Hassani · Alejandro Ribeiro -
2021 Poster: Offline RL Without Off-Policy Evaluation »
David Brandfonbrener · Will Whitney · Rajesh Ranganath · Joan Bruna -
2021 Poster: Online learning in MDPs with linear function approximation and bandit feedback. »
Gergely Neu · Iuliia Olkhovskaia -
2020 : Counterfactuals and Offline RL »
Emma Brunskill -
2020 : Q & A and Panel Session with Dan Weld, Kristen Grauman, Scott Yih, Emma Brunskill, and Alex Ratner »
Kristen Grauman · Wen-tau Yih · Alexander Ratner · Emma Brunskill · Douwe Kiela · Daniel S. Weld -
2020 : Closing remarks »
Raymond Chua · Feryal Behbahani · Julie J Lee · Rui Ponte Costa · Doina Precup · Blake Richards · Ida Momennejad -
2020 : Invited Talk #7 QnA - Yael Niv »
Yael Niv · Doina Precup · Raymond Chua · Feryal Behbahani -
2020 : Speaker Introduction: Yael Niv »
Doina Precup · Raymond Chua · Feryal Behbahani -
2020 : Orals 1.2: ZORB: A Derivative-Free Backpropagation Algorithm for Neural Networks »
Varun Ranganathan · Alex Lewandowski -
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 : Causal Structure Discovery in RL »
John Langford -
2020 : Mini-panel discussion 1 - Bridging the gap between theory and practice »
Aviv Tamar · Emma Brunskill · Jost Tobias Springenberg · Omer Gottesman · Daniel Mankowitz -
2020 : Keynote: Emma Brunskill »
Emma Brunskill -
2020 : Poster Session B »
Ravichandra Addanki · Andreea-Ioana Deac · Yujia Xie · Francesco Landolfi · Antoine Prouvost · Claudius Gros · Renzo Massobrio · Abhishek Cauligi · Simon Alford · Hanjun Dai · Alberto Franzin · Nitish Kumar Panigrahy · Brandon Kates · Iddo Drori · Taoan Huang · Zhou Zhou · Marin Vlastelica · Anselm Paulus · Aaron Zweig · Minsu Cho · Haiyan Yin · Michal Lisicki · Nan Jiang · Haoran Sun -
2020 Workshop: Offline Reinforcement Learning »
Aviral Kumar · Rishabh Agarwal · George Tucker · Lihong Li · Doina Precup · Aviral Kumar -
2020 : Panel Discussions »
Grace Lindsay · George Konidaris · Shakir Mohamed · Kimberly Stachenfeld · Peter Dayan · Yael Niv · Doina Precup · Catherine Hartley · Ishita Dasgupta -
2020 Workshop: Biological and Artificial Reinforcement Learning »
Raymond Chua · Feryal Behbahani · Julie J Lee · Sara Zannone · Rui Ponte Costa · Blake Richards · Ida Momennejad · Doina Precup -
2020 : Organizers Opening Remarks »
Raymond Chua · Feryal Behbahani · Julie J Lee · Ida Momennejad · Rui Ponte Costa · Blake Richards · Doina Precup -
2020 : Panel discussion on minimizing bias in machine learning in education »
Neil Heffernan · Osonde A. Osoba · Emma Brunskill · Kathi Fisler -
2020 : Keynote: Doina Precup »
Doina Precup -
2020 : Climate Change and ML in the Private Sector »
Aisha Walcott-Bryant · Lea Boche · Anima Anandkumar -
2020 Workshop: Machine Learning and the Physical Sciences »
Anima Anandkumar · Kyle Cranmer · Shirley Ho · Mr. Prabhat · Lenka Zdeborová · Atilim Gunes Baydin · Juan Carrasquilla · Adji Bousso Dieng · Karthik Kashinath · Gilles Louppe · Brian Nord · Michela Paganini · Savannah Thais -
2020 Poster: Sinkhorn Natural Gradient for Generative Models »
Zebang Shen · Zhenfu Wang · Alejandro Ribeiro · Hamed Hassani -
2020 Poster: Sinkhorn Barycenter via Functional Gradient Descent »
Zebang Shen · Zhenfu Wang · Alejandro Ribeiro · Hamed Hassani -
2020 Spotlight: Sinkhorn Natural Gradient for Generative Models »
Zebang Shen · Zhenfu Wang · Alejandro Ribeiro · Hamed Hassani -
2020 Poster: A mean-field analysis of two-player zero-sum games »
Carles Domingo-Enrich · Samy Jelassi · Arthur Mensch · Grant Rotskoff · Joan Bruna -
2020 Poster: Empirical Likelihood for Contextual Bandits »
Nikos Karampatziakis · John Langford · Paul Mineiro -
2020 Poster: Can Graph Neural Networks Count Substructures? »
Zhengdao Chen · Lei Chen · Soledad Villar · Joan Bruna -
2020 Poster: Graphon Neural Networks and the Transferability of Graph Neural Networks »
Luana Ruiz · Luiz Chamon · Alejandro Ribeiro -
2020 Poster: Geometric Exploration for Online Control »
Orestis Plevrakis · Elad Hazan -
2020 Poster: Reward Propagation Using Graph Convolutional Networks »
Martin Klissarov · Doina Precup -
2020 Poster: On the Convergence of Smooth Regularized Approximate Value Iteration Schemes »
Elena Smirnova · Elvis Dohmatob -
2020 Poster: Instance Based Approximations to Profile Maximum Likelihood »
Nima Anari · Moses Charikar · Kirankumar Shiragur · Aaron Sidford -
2020 Poster: Learning compositional functions via multiplicative weight updates »
Jeremy Bernstein · Jiawei Zhao · Markus Meister · Ming-Yu Liu · Anima Anandkumar · Yisong Yue -
2020 Spotlight: On the Convergence of Smooth Regularized Approximate Value Iteration Schemes »
Elena Smirnova · Elvis Dohmatob -
2020 Spotlight: Reward Propagation Using Graph Convolutional Networks »
Martin Klissarov · Doina Precup -
2020 Session: Orals & Spotlights Track 26: Graph/Relational/Theory »
Joan Bruna · Cassio de Campos -
2020 Poster: Gradient Surgery for Multi-Task Learning »
Tianhe Yu · Saurabh Kumar · Abhishek Gupta · Sergey Levine · Karol Hausman · Chelsea Finn -
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 Poster: Dirichlet Graph Variational Autoencoder »
Jia Li · Jianwei Yu · Jiajin Li · Honglei Zhang · Kangfei Zhao · Yu Rong · Hong Cheng · Junzhou Huang -
2020 Poster: Non-Stochastic Control with Bandit Feedback »
Paula Gradu · John Hallman · Elad Hazan -
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: Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine »
Jiajin Li · Caihua Chen · Anthony Man-Cho So -
2020 Poster: Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping »
Eduard Gorbunov · Marina Danilova · Alexander Gasnikov -
2020 Poster: Linearly Converging Error Compensated SGD »
Eduard Gorbunov · Dmitry Kovalev · Dmitry Makarenko · Peter Richtarik -
2020 Poster: IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method »
Yossi Arjevani · Joan Bruna · Bugra Can · Mert Gurbuzbalaban · Stefanie Jegelka · Hongzhou Lin -
2020 Poster: Acceleration with a Ball Optimization Oracle »
Yair Carmon · Arun Jambulapati · Qijia Jiang · Yujia Jin · Yin Tat Lee · Aaron Sidford · Kevin Tian -
2020 Poster: Large-Scale Methods for Distributionally Robust Optimization »
Daniel Levy · Yair Carmon · John Duchi · Aaron Sidford -
2020 Poster: Neural Networks with Recurrent Generative Feedback »
Yujia Huang · James Gornet · Sihui Dai · Zhiding Yu · Tan Nguyen · Doris Tsao · Anima Anandkumar -
2020 Poster: Causal Discovery in Physical Systems from Videos »
Yunzhu Li · Antonio Torralba · Anima Anandkumar · Dieter Fox · Animesh Garg -
2020 Poster: Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning »
Weili Nie · Zhiding Yu · Lei Mao · Ankit Patel · Yuke Zhu · Anima Anandkumar -
2020 Spotlight: Linearly Converging Error Compensated SGD »
Eduard Gorbunov · Dmitry Kovalev · Dmitry Makarenko · Peter Richtarik -
2020 Spotlight: Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning »
Weili Nie · Zhiding Yu · Lei Mao · Ankit Patel · Yuke Zhu · Anima Anandkumar -
2020 Spotlight: IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method »
Yossi Arjevani · Joan Bruna · Bugra Can · Mert Gurbuzbalaban · Stefanie Jegelka · Hongzhou Lin -
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 Oral: Acceleration with a Ball Optimization Oracle »
Yair Carmon · Arun Jambulapati · Qijia Jiang · Yujia Jin · Yin Tat Lee · Aaron Sidford · Kevin Tian -
2020 : Research at NVIDIA: New Core AI and Machine Learning Lab »
Anima Anandkumar -
2020 Poster: Multipole Graph Neural Operator for Parametric Partial Differential Equations »
Zongyi Li · Nikola Kovachki · Kamyar Azizzadenesheli · Burigede Liu · Andrew Stuart · Kaushik Bhattacharya · Anima Anandkumar -
2020 Poster: TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search »
Tarun Gogineni · Ziping Xu · Exequiel Punzalan · Runxuan Jiang · Joshua Kammeraad · Ambuj Tewari · Paul Zimmerman -
2020 Poster: Differentiable Meta-Learning of Bandit Policies »
Craig Boutilier · Chih-wei Hsu · Branislav Kveton · Martin Mladenov · Csaba Szepesvari · Manzil Zaheer -
2020 Poster: Latent Bandits Revisited »
Joey Hong · Branislav Kveton · Manzil Zaheer · Yinlam Chow · Amr Ahmed · Craig Boutilier -
2020 Poster: Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes »
Zaiwei Chen · Siva Theja Maguluri · Sanjay Shakkottai · Karthikeyan Shanmugam -
2020 Poster: Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting »
Ziping Xu · Ambuj Tewari -
2020 Poster: Adversarially Robust Streaming Algorithms via Differential Privacy »
Avinatan Hassidim · Haim Kaplan · Yishay Mansour · Yossi Matias · Uri Stemmer -
2020 Poster: Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding »
Hongseok Namkoong · Ramtin Keramati · Steve Yadlowsky · Emma Brunskill -
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: Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity »
Haim Kaplan · Yishay Mansour · Uri Stemmer · Eliad Tsfadia -
2020 Poster: An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay »
Scott Fujimoto · David Meger · Doina Precup -
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: Efficient Contextual Bandits with Continuous Actions »
Maryam Majzoubi · Chicheng Zhang · Rajan Chari · Akshay Krishnamurthy · John Langford · Aleksandrs Slivkins -
2020 Poster: Online Agnostic Boosting via Regret Minimization »
Nataly Brukhim · Xinyi Chen · Elad Hazan · Shay Moran -
2020 Poster: Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing »
Arthur Delarue · Ross Anderson · Christian Tjandraatmadja -
2020 Poster: A Dynamical Central Limit Theorem for Shallow Neural Networks »
Zhengdao Chen · Grant Rotskoff · Joan Bruna · Eric Vanden-Eijnden -
2020 Poster: Learning the Linear Quadratic Regulator from Nonlinear Observations »
Zakaria Mhammedi · Dylan Foster · Max Simchowitz · Dipendra Misra · Wen Sun · Akshay Krishnamurthy · Alexander Rakhlin · John Langford -
2020 Poster: Forethought and Hindsight in Credit Assignment »
Veronica Chelu · Doina Precup · Hado van Hasselt -
2020 Poster: Escaping the Gravitational Pull of Softmax »
Jincheng Mei · Chenjun Xiao · Bo Dai · Lihong Li · Csaba Szepesvari · Dale Schuurmans -
2020 Poster: The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification »
Christian Tjandraatmadja · Ross Anderson · Joey Huchette · Will Ma · KRUNAL KISHOR PATEL · Juan Pablo Vielma -
2020 Poster: Convolutional Tensor-Train LSTM for Spatio-Temporal Learning »
Jiahao Su · Wonmin Byeon · Jean Kossaifi · Furong Huang · Jan Kautz · Anima Anandkumar -
2020 Poster: Probably Approximately Correct Constrained Learning »
Luiz Chamon · Alejandro Ribeiro -
2020 Poster: Adaptive Discretization for Model-Based Reinforcement Learning »
Sean Sinclair · Tianyu Wang · Gauri Jain · Siddhartha Banerjee · Christina Yu -
2020 Poster: Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration »
Andrea Zanette · Alessandro Lazaric · Mykel J Kochenderfer · Emma Brunskill -
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: 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: 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: On the Equivalence between Online and Private Learnability beyond Binary Classification »
Young H Jung · Baekjin Kim · Ambuj Tewari -
2020 Poster: Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach »
Luofeng Liao · You-Lin Chen · Zhuoran Yang · Bo Dai · Mladen Kolar · Zhaoran Wang -
2020 Poster: Robust Multi-Agent Reinforcement Learning with Model Uncertainty »
Kaiqing Zhang · TAO SUN · Yunzhe Tao · Sahika Genc · Sunil Mallya · Tamer Basar -
2020 Poster: Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems »
Sahin Lale · Kamyar Azizzadenesheli · Babak Hassibi · Anima Anandkumar -
2020 Poster: Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes »
Dongsheng Ding · Kaiqing Zhang · Tamer Basar · Mihailo Jovanovic -
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: 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: On the Equivalence between Online and Private Learnability beyond Binary Classification »
Young H Jung · Baekjin Kim · Ambuj Tewari -
2020 : Prof. Anima Anandkumar (California Institute of Technology and NVIDIA) »
Anima Anandkumar -
2020 : Real World RL with Vowpal Wabbit: Beyond Contextual Bandits »
John Langford · Marek Wydmuch · Maryam Majzoubi · Adith Swaminathan · · Dylan Foster · Paul Mineiro -
2019 : Logarithmic Regret for Online Control »
Naman Agarwal · Elad Hazan · Karan Singh -
2019 : Panel Session: A new hope for neuroscience »
Yoshua Bengio · Blake Richards · Timothy Lillicrap · Ila Fiete · David Sussillo · Doina Precup · Konrad Kording · Surya Ganguli -
2019 : Continuous Online Learning and New Insights to Online Imitation Learning »
Jonathan Lee · Ching-An Cheng · Ken Goldberg · Byron Boots -
2019 : Emma Brünskill, "Some Theory RL Challenges Inspired by Education" »
Emma Brunskill -
2019 : Poster Session »
Pravish Sainath · Mohamed Akrout · Charles Delahunt · Nathan Kutz · Guangyu Robert Yang · Joseph Marino · L F Abbott · Nicolas Vecoven · Damien Ernst · andrew warrington · Michael Kagan · Kyunghyun Cho · Kameron Harris · Leopold Grinberg · John J. Hopfield · Dmitry Krotov · Taliah Muhammad · Erick Cobos · Edgar Walker · Jacob Reimer · Andreas Tolias · Alexander Ecker · Janaki Sheth · Yu Zhang · Maciej Wołczyk · Jacek Tabor · Szymon Maszke · Roman Pogodin · Dane Corneil · Wulfram Gerstner · Baihan Lin · Guillermo Cecchi · Jenna M Reinen · Irina Rish · Guillaume Bellec · Darjan Salaj · Anand Subramoney · Wolfgang Maass · Yueqi Wang · Ari Pakman · Jin Hyung Lee · Liam Paninski · Bryan Tripp · Colin Graber · Alex Schwing · Luke Prince · Gabriel Ocker · Michael Buice · Benjamin Lansdell · Konrad Kording · Jack Lindsey · Terrence Sejnowski · Matthew Farrell · Eric Shea-Brown · Nicolas Farrugia · Victor Nepveu · Jiwoong Im · Kristin Branson · Brian Hu · Ramakrishnan Iyer · Stefan Mihalas · Sneha Aenugu · Hananel Hazan · Sihui Dai · Tan Nguyen · Doris Tsao · Richard Baraniuk · Anima Anandkumar · Hidenori Tanaka · Aran Nayebi · Stephen Baccus · Surya Ganguli · Dean Pospisil · Eilif Muller · Jeffrey S Cheng · Gaël Varoquaux · Kamalaker Dadi · Dimitrios C Gklezakos · Rajesh PN Rao · Anand Louis · Christos Papadimitriou · Santosh Vempala · Naganand Yadati · Daniel Zdeblick · Daniela M Witten · Nicholas Roberts · Vinay Prabhu · Pierre Bellec · Poornima Ramesh · Jakob H Macke · Santiago Cadena · Guillaume Bellec · Franz Scherr · Owen Marschall · Robert Kim · Hannes Rapp · Marcio Fonseca · Oliver Armitage · Jiwoong Im · Thomas Hardcastle · Abhishek Sharma · Wyeth Bair · Adrian Valente · Shane Shang · Merav Stern · Rutuja Patil · Peter Wang · Sruthi Gorantla · Peter Stratton · Tristan Edwards · Jialin Lu · Martin Ester · Yurii Vlasov · Siavash Golkar -
2019 : Panel - The Role of Communication at Large: Aparna Lakshmiratan, Jason Yosinski, Been Kim, Surya Ganguli, Finale Doshi-Velez »
Aparna Lakshmiratan · Finale Doshi-Velez · Surya Ganguli · Zachary Lipton · Michela Paganini · Anima Anandkumar · Jason Yosinski -
2019 : Poster Spotlights »
Théophile Griveau-Billion · Rahul Singh · Zichen Zhang · Ciarán Lee · Jesse Krijthe · Grace Charles · Vira Semenova · Rahul Ladhania · Miruna Oprescu -
2019 : Afternoon Coffee Break & Poster Session »
Heidi Komkov · Stanislav Fort · Zhaoyou Wang · Rose Yu · Ji Hwan Park · Samuel Schoenholz · Taoli Cheng · Ryan-Rhys Griffiths · Chase Shimmin · Surya Karthik Mukkavili · Philippe Schwaller · Christian Knoll · Yangzesheng Sun · Keiichi Kisamori · Gavin Graham · Gavin Portwood · Hsin-Yuan Huang · Paul Novello · Moritz Munchmeyer · Anna Jungbluth · Daniel Levine · Ibrahim Ayed · Steven Atkinson · Jan Hermann · Peter Grönquist · · Priyabrata Saha · Yannik Glaser · Lingge Li · Yutaro Iiyama · Rushil Anirudh · Maciej Koch-Janusz · Vikram Sundar · Francois Lanusse · Auralee Edelen · Jonas Köhler · Jacky H. T. Yip · jiadong guo · Xiangyang Ju · Adi Hanuka · Adrian Albert · Valentina Salvatelli · Mauro Verzetti · Javier Duarte · Eric Moreno · Emmanuel de Bézenac · Athanasios Vlontzos · Alok Singh · Thomas Klijnsma · Brad Neuberg · Paul Wright · Mustafa Mustafa · David Schmidt · Steven Farrell · Hao Sun -
2019 : Poster and Coffee Break 2 »
Karol Hausman · Kefan Dong · Ken Goldberg · Lihong Li · Lin Yang · Lingxiao Wang · Lior Shani · Liwei Wang · Loren Amdahl-Culleton · Lucas Cassano · Marc Dymetman · Marc Bellemare · Marcin Tomczak · Margarita Castro · Marius Kloft · Marius-Constantin Dinu · Markus Holzleitner · Martha White · Mengdi Wang · Michael Jordan · Mihailo Jovanovic · Ming Yu · Minshuo Chen · Moonkyung Ryu · Muhammad Zaheer · Naman Agarwal · Nan Jiang · Niao He · Nikolaus Yasui · Nikos Karampatziakis · Nino Vieillard · Ofir Nachum · Olivier Pietquin · Ozan Sener · Pan Xu · Parameswaran Kamalaruban · Paul Mineiro · Paul Rolland · Philip Amortila · Pierre-Luc Bacon · Prakash Panangaden · Qi Cai · Qiang Liu · Quanquan Gu · Raihan Seraj · Richard Sutton · Rick Valenzano · Robert Dadashi · Rodrigo Toro Icarte · Roshan Shariff · Roy Fox · Ruosong Wang · Saeed Ghadimi · Samuel Sokota · Sean Sinclair · Sepp Hochreiter · Sergey Levine · Sergio Valcarcel Macua · Sham Kakade · Shangtong Zhang · Sheila McIlraith · Shie Mannor · Shimon Whiteson · Shuai Li · Shuang Qiu · Wai Lok Li · Siddhartha Banerjee · Sitao Luan · Tamer Basar · Thinh Doan · Tianhe Yu · Tianyi Liu · Tom Zahavy · Toryn Klassen · Tuo Zhao · Vicenç Gómez · Vincent Liu · Volkan Cevher · Wesley Suttle · Xiao-Wen Chang · Xiaohan Wei · Xiaotong Liu · Xingguo Li · Xinyi Chen · Xingyou Song · Yao Liu · YiDing Jiang · Yihao Feng · Yilun Du · Yinlam Chow · Yinyu Ye · Yishay Mansour · · Yonathan Efroni · Yongxin Chen · Yuanhao Wang · Bo Dai · Chen-Yu Wei · Harsh Shrivastava · Hongyang Zhang · Qinqing Zheng · SIDDHARTHA SATPATHI · Xueqing Liu · Andreu Vall -
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 : 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 : Invited Talk »
Emma Brunskill -
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 : Panel Discussion »
Richard Sutton · Doina Precup -
2019 : Coffee break, posters, and 1-on-1 discussions »
Yangyi Lu · Daniel Chen · Hongseok Namkoong · Marie Charpignon · Maja Rudolph · Amanda Coston · Julius von Kügelgen · Niranjani Prasad · Paramveer Dhillon · Yunzong Xu · Yixin Wang · Alexander Markham · David Rohde · Rahul Singh · Zichen Zhang · Negar Hassanpour · Ankit Sharma · Ciarán Lee · Jean Pouget-Abadie · Jesse Krijthe · Divyat Mahajan · Nan Rosemary Ke · Peter Wirnsberger · Vira Semenova · Dmytro Mykhaylov · Dennis Shen · Kenta Takatsu · Liyang Sun · Jeremy Yang · Alexander Franks · Pak Kan Wong · Tauhid Zaman · Shira Mitchell · min kyoung kang · Qi Yang -
2019 : Surya Ganguli, Yasaman Bahri, Florent Krzakala moderated by Lenka Zdeborova »
Florent Krzakala · Yasaman Bahri · Surya Ganguli · Lenka Zdeborová · Adji Bousso Dieng · Joan Bruna -
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 : Invited Talk: Hierarchical Reinforcement Learning: Computational Advances and Neuroscience Connections »
Doina Precup -
2019 : Opening Remarks »
Atilim Gunes Baydin · Juan Carrasquilla · Shirley Ho · Karthik Kashinath · Michela Paganini · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Roger Melko · Mr. Prabhat · Frank Wood -
2019 Workshop: Machine Learning and the Physical Sciences »
Atilim Gunes Baydin · Juan Carrasquilla · Shirley Ho · Karthik Kashinath · Michela Paganini · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Roger Melko · Mr. Prabhat · Frank Wood -
2019 Workshop: Deep Reinforcement Learning »
Pieter Abbeel · Chelsea Finn · Joelle Pineau · David Silver · Satinder Singh · Joshua Achiam · Carlos Florensa · Christopher Grimm · Haoran Tang · Vivek Veeriah -
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 : Poster Session »
Gergely Flamich · Shashanka Ubaru · Charles Zheng · Josip Djolonga · Kristoffer Wickstrøm · Diego Granziol · Konstantinos Pitas · Jun Li · Robert Williamson · Sangwoong Yoon · Kwot Sin Lee · Julian Zilly · Linda Petrini · Ian Fischer · Zhe Dong · Alexander Alemi · Bao-Ngoc Nguyen · Rob Brekelmans · Tailin Wu · Aditya Mahajan · Alexander Li · Kirankumar Shiragur · Yair Carmon · Linara Adilova · SHIYU LIU · Bang An · Sanjeeb Dash · Oktay Gunluk · Arya Mazumdar · Mehul Motani · Julia Rosenzweig · Michael Kamp · Marton Havasi · Leighton P Barnes · Zhengqing Zhou · Yi Hao · Dylan Foster · Yuval Benjamini · Nati Srebro · Michael Tschannen · Paul Rubenstein · Sylvain Gelly · John Duchi · Aaron Sidford · Robin Ru · Stefan Zohren · Murtaza Dalal · Michael A Osborne · Stephen J Roberts · Moses Charikar · Jayakumar Subramanian · Xiaodi Fan · Max Schwarzer · Nicholas Roberts · Simon Lacoste-Julien · Vinay Prabhu · Aram Galstyan · Greg Ver Steeg · Lalitha Sankar · Yung-Kyun Noh · Gautam Dasarathy · Frank Park · Ngai-Man (Man) Cheung · Ngoc-Trung Tran · Linxiao Yang · Ben Poole · Andrea Censi · Tristan Sylvain · R Devon Hjelm · Bangjie Liu · Jose Gallego-Posada · Tyler Sypherd · Kai Yang · Jan Nikolas Morshuis -
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 : 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 : Poster Session »
Ahana Ghosh · Javad Shafiee · Akhilan Boopathy · Alex Tamkin · Theodoros Vasiloudis · Vedant Nanda · Ali Baheri · Paul Fieguth · Andrew Bennett · Guanya Shi · Hao Liu · Arushi Jain · Jacob Tyo · Benjie Wang · Boxiao Chen · Carroll Wainwright · Chandramouli Shama Sastry · Chao Tang · Daniel S. Brown · David Inouye · David Venuto · Dhruv Ramani · Dimitrios Diochnos · Divyam Madaan · Dmitrii Krashenikov · Joel Oren · Doyup Lee · Eleanor Quint · elmira amirloo · Matteo Pirotta · Gavin Hartnett · Geoffroy Dubourg-Felonneau · Gokul Swamy · Pin-Yu Chen · Ilija Bogunovic · Jason Carter · Javier Garcia-Barcos · Jeet Mohapatra · Jesse Zhang · Jian Qian · John Martin · Oliver Richter · Federico Zaiter · Tsui-Wei Weng · Karthik Abinav Sankararaman · Kyriakos Polymenakos · Lan Hoang · mahdieh abbasi · Marco Gallieri · Mathieu Seurin · Matteo Papini · Matteo Turchetta · Matthew Sotoudeh · Mehrdad Hosseinzadeh · Nathan Fulton · Masatoshi Uehara · Niranjani Prasad · Oana-Maria Camburu · Patrik Kolaric · Philipp Renz · Prateek Jaiswal · Reazul Hasan Russel · Riashat Islam · Rishabh Agarwal · Alexander Aldrick · Sachin Vernekar · Sahin Lale · Sai Kiran Narayanaswami · Samuel Daulton · Sanjam Garg · Sebastian East · Shun Zhang · Soheil Dsidbari · Justin Goodwin · Victoria Krakovna · Wenhao Luo · Wesley Chung · Yuanyuan Shi · Yuh-Shyang Wang · Hongwei Jin · Ziping Xu -
2019 : Robust One-Bit Recovery via ReLU Generative Networks: Improved Statistical Rate and Global Landscape Analysis »
Shuang Qiu · Xiaohan Wei · Zhuoran Yang -
2019 : Opening Remarks »
Raymond Chua · Feryal Behbahani · Sara Zannone · Rui Ponte Costa · Claudia Clopath · Doina Precup · Blake Richards -
2019 : Opening Remarks »
Reinhard Heckel · Paul Hand · Alex Dimakis · Joan Bruna · Deanna Needell · Richard Baraniuk -
2019 Workshop: Biological and Artificial Reinforcement Learning »
Raymond Chua · Sara Zannone · Feryal Behbahani · Rui Ponte Costa · Claudia Clopath · Blake Richards · Doina Precup -
2019 Workshop: Solving inverse problems with deep networks: New architectures, theoretical foundations, and applications »
Reinhard Heckel · Paul Hand · Richard Baraniuk · Joan Bruna · Alex Dimakis · Deanna Needell -
2019 Poster: Gradient Dynamics of Shallow Univariate ReLU Networks »
Francis Williams · Matthew Trager · Daniele Panozzo · Claudio Silva · Denis Zorin · Joan Bruna -
2019 Poster: On the Expressive Power of Deep Polynomial Neural Networks »
Joe Kileel · Matthew Trager · Joan Bruna -
2019 Poster: A General Framework for Symmetric Property Estimation »
Moses Charikar · Kirankumar Shiragur · Aaron Sidford -
2019 Poster: Generalization Bounds in the Predict-then-Optimize Framework »
Othman El Balghiti · Adam N. Elmachtoub · Paul Grigas · Ambuj Tewari -
2019 Poster: Offline Contextual Bandits with High Probability Fairness Guarantees »
Blossom Metevier · Stephen Giguere · Sarah Brockman · Ari Kobren · Yuriy Brun · Emma Brunskill · Philip Thomas -
2019 Poster: Private Learning Implies Online Learning: An Efficient Reduction »
Alon Gonen · Elad Hazan · Shay Moran -
2019 Spotlight: Private Learning Implies Online Learning: An Efficient Reduction »
Alon Gonen · Elad Hazan · Shay Moran -
2019 Poster: Variance Reduction for Matrix Games »
Yair Carmon · Yujia Jin · Aaron Sidford · Kevin Tian -
2019 Poster: Competitive Gradient Descent »
Florian Schaefer · Anima Anandkumar -
2019 Poster: Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias »
Stéphane d'Ascoli · Levent Sagun · Giulio Biroli · Joan Bruna -
2019 Poster: Maximum Entropy Monte-Carlo Planning »
Chenjun Xiao · Ruitong Huang · Jincheng Mei · Dale Schuurmans · Martin Müller -
2019 Poster: Efficient Forward Architecture Search »
Hanzhang Hu · John Langford · Rich Caruana · Saurajit Mukherjee · Eric Horvitz · Debadeepta Dey -
2019 Poster: Statistical-Computational Tradeoff in Single Index Models »
Lingxiao Wang · Zhuoran Yang · Zhaoran Wang -
2019 Poster: RUDDER: Return Decomposition for Delayed Rewards »
Jose A. Arjona-Medina · Michael Gillhofer · Michael Widrich · Thomas Unterthiner · Johannes Brandstetter · Sepp Hochreiter -
2019 Oral: Variance Reduction for Matrix Games »
Yair Carmon · Yujia Jin · Aaron Sidford · Kevin Tian -
2019 Poster: Stein Variational Gradient Descent With Matrix-Valued Kernels »
Dilin Wang · Ziyang Tang · Chandrajit Bajaj · Qiang Liu -
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: Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy »
Boyi Liu · Qi Cai · Zhuoran Yang · Zhaoran Wang -
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: Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric »
Nirandika Wanigasekara · Christina Yu -
2019 Poster: On the equivalence between graph isomorphism testing and function approximation with GNNs »
Zhengdao Chen · Soledad Villar · Lei Chen · Joan Bruna -
2019 Poster: Online Learning via the Differential Privacy Lens »
Jacob Abernethy · Young H Jung · Chansoo Lee · Audra McMillan · Ambuj Tewari -
2019 Poster: Unsupervised Curricula for Visual Meta-Reinforcement Learning »
Allan Jabri · Kyle Hsu · Abhishek Gupta · Benjamin Eysenbach · Sergey Levine · Chelsea Finn -
2019 Spotlight: Online Learning via the Differential Privacy Lens »
Jacob Abernethy · Young H Jung · Chansoo Lee · Audra McMillan · Ambuj Tewari -
2019 Poster: Variance Reduced Policy Evaluation with Smooth Function Approximation »
Hoi-To Wai · Mingyi Hong · Zhuoran Yang · Zhaoran Wang · Kexin Tang -
2019 Poster: Constrained Reinforcement Learning Has Zero Duality Gap »
Santiago Paternain · Luiz Chamon · Miguel Calvo-Fullana · Alejandro Ribeiro -
2019 Poster: Learning to Screen »
Alon Cohen · Avinatan Hassidim · Haim Kaplan · Yishay Mansour · Shay Moran -
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: Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG »
Yujia Jin · Aaron Sidford -
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: Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG »
Yujia Jin · Aaron Sidford -
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 Poster: Meta-Learning with Implicit Gradients »
Aravind Rajeswaran · Chelsea Finn · Sham Kakade · Sergey Levine -
2019 Poster: A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization »
Sulaiman Alghunaim · Kun Yuan · Ali H Sayed -
2019 Poster: Convergent Policy Optimization for Safe Reinforcement Learning »
Ming Yu · Zhuoran Yang · Mladen Kolar · Zhaoran Wang -
2019 Poster: A First-Order Algorithmic Framework for Wasserstein Distributionally Robust Logistic Regression »
Jiajin Li · SEN HUANG · Anthony Man-Cho So -
2019 Poster: Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model »
Andrea Zanette · Mykel J Kochenderfer · Emma Brunskill -
2019 Poster: Stability of Graph Scattering Transforms »
Fernando Gama · Alejandro Ribeiro · Joan Bruna -
2019 Poster: Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems »
Young H Jung · Ambuj Tewari -
2019 Poster: Non-Cooperative Inverse Reinforcement Learning »
Xiangyuan Zhang · Kaiqing Zhang · Erik Miehling · Tamer Basar -
2019 Poster: Worst-Case Regret Bounds for Exploration via Randomized Value Functions »
Daniel Russo -
2019 Poster: Invertible Convolutional Flow »
Mahdi Karami · Dale Schuurmans · Jascha Sohl-Dickstein · Laurent Dinh · Daniel Duckworth -
2019 Poster: Guided Meta-Policy Search »
Russell Mendonca · Abhishek Gupta · Rosen Kralev · Pieter Abbeel · Sergey Levine · Chelsea Finn -
2019 Poster: Complexity of Highly Parallel Non-Smooth Convex Optimization »
Sebastien Bubeck · Qijia Jiang · Yin-Tat Lee · Yuanzhi Li · Aaron Sidford -
2019 Demonstration: AI in Two-sided Ride-sharing Marketplace »
Zhiwei Qin · Shikai Luo · lingyu zhang · yan jiao · Xiaocheng Tang · Lulu Zhang · hongtu zhu · Jieping Ye -
2019 Spotlight: Guided Meta-Policy Search »
Russell Mendonca · Abhishek Gupta · Rosen Kralev · Pieter Abbeel · Sergey Levine · Chelsea Finn -
2019 Spotlight: Complexity of Highly Parallel Non-Smooth Convex Optimization »
Sebastien Bubeck · Qijia Jiang · Yin-Tat Lee · Yuanzhi Li · Aaron Sidford -
2019 Spotlight: Invertible Convolutional Flow »
Mahdi Karami · Dale Schuurmans · Jascha Sohl-Dickstein · Laurent Dinh · Daniel Duckworth -
2019 Poster: Logarithmic Regret for Online Control »
Naman Agarwal · Elad Hazan · Karan Singh -
2019 Poster: On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems »
Baekjin Kim · Ambuj Tewari -
2019 Poster: Limiting Extrapolation in Linear Approximate Value Iteration »
Andrea Zanette · Alessandro Lazaric · Mykel J Kochenderfer · Emma Brunskill -
2019 Poster: Learning Nonsymmetric Determinantal Point Processes »
Mike Gartrell · Victor-Emmanuel Brunel · Elvis Dohmatob · Syrine Krichene -
2019 Poster: A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport »
Arun Jambulapati · Aaron Sidford · Kevin Tian -
2019 Oral: Logarithmic Regret for Online Control »
Naman Agarwal · Elad Hazan · Karan Singh -
2018 : Spotlights & Poster Session »
James A Preiss · Alexander Grishin · Ville Kyrki · Pol Moreno Comellas · Akshay Narayan · Tze-Yun Leong · Yongxi Tan · Lilian Weng · Toshiharu Sugawara · Kenny Young · Tianmin Shu · Jonas Gehring · Ahmad Beirami · Chris Amato · sammie katt · Andrea Baisero · Arseny Kuznetsov · Jan Humplik · Vladimír Petrík -
2018 : Invited Talk 3 »
Joan Bruna -
2018 : Learning to Learn from Imperfect Demonstrations »
Ge Yang · Chelsea Finn -
2018 Workshop: Integration of Deep Learning Theories »
Richard Baraniuk · Anima Anandkumar · Stephane Mallat · Ankit Patel · nhật Hồ -
2018 : Chelsea Finn (UCBerkeley / Google Brain): Learning Generalizable Behavior through Unsupervised Interaction »
Chelsea Finn -
2018 : Joan Bruna »
Joan Bruna -
2018 : Byron Boots »
Byron Boots -
2018 : Poster Session »
Lorenzo Masoero · Tammo Rukat · Runjing Liu · Sayak Ray Chowdhury · Daniel Coelho de Castro · Claudia Wehrhahn · Feras Saad · Archit Verma · Kelvin Hsu · Irineo Cabreros · Sandhya Prabhakaran · Yiming Sun · Maxime Rischard · Linfeng Liu · Adam Farooq · Jeremiah Liu · Melanie F. Pradier · Diego Romeres · Neill Campbell · Kai Xu · Mehmet M Dundar · Tucker Keuter · Prashnna Gyawali · Eli Sennesh · Alessandro De Palma · Daniel Flam-Shepherd · Takatomi Kubo -
2018 : Talk 4: Chelsea Finn - An agent that can do many things
(by modeling the world) »
Chelsea Finn -
2018 : Coffee Break 1 (Posters) »
Ananya Kumar · Siyu Huang · Huazhe Xu · Michael Janner · Parth Chadha · Nils Thuerey · Peter Lu · Maria Bauza · Anthony Tompkins · Guanya Shi · Thomas Baumeister · André Ofner · Zhi-Qi Cheng · Yuping Luo · Deepika Bablani · Jeroen Vanbaar · Kartic Subr · Tatiana López-Guevara · Devesh Jha · Fabian Fuchs · Stefano Rosa · Alison Pouplin · Alex Ray · Qi Liu · Eric Crawford -
2018 : Invited Speaker #1 Chelsea Finn »
Chelsea Finn -
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 Poster: Active Learning for Non-Parametric Regression Using Purely Random Trees »
Jack Goetz · Ambuj Tewari · Paul Zimmerman -
2018 Poster: Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression »
Neha Gupta · Aaron Sidford -
2018 Poster: Online Improper Learning with an Approximation Oracle »
Elad Hazan · Wei Hu · Yuanzhi Li · Zhiyuan Li -
2018 Poster: Approximating Real-Time Recurrent Learning with Random Kronecker Factors »
Asier Mujika · Florian Meier · Angelika Steger -
2018 Poster: Differentiable MPC for End-to-end Planning and Control »
Brandon Amos · Ivan Jimenez · Jacob I Sacks · Byron Boots · J. Zico Kolter -
2018 Poster: Non-delusional Q-learning and value-iteration »
Tyler Lu · Dale Schuurmans · Craig Boutilier -
2018 Poster: Contrastive Learning from Pairwise Measurements »
Yi Chen · Zhuoran Yang · Yuchen Xie · Zhaoran Wang -
2018 Oral: Non-delusional Q-learning and value-iteration »
Tyler Lu · Dale Schuurmans · Craig Boutilier -
2018 Poster: But How Does It Work in Theory? Linear SVM with Random Features »
Yitong Sun · Anna Gilbert · Ambuj Tewari -
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: Learning and Inference in Hilbert Space with Quantum Graphical Models »
Siddarth Srinivasan · Carlton Downey · Byron Boots -
2018 Poster: Temporal Regularization for Markov Decision Process »
Pierre Thodoroff · Audrey Durand · Joelle Pineau · Doina Precup -
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: Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation »
Qiang Liu · Lihong Li · Ziyang Tang · Denny Zhou -
2018 Poster: Dual Policy Iteration »
Wen Sun · Geoffrey Gordon · Byron Boots · J. Bagnell -
2018 Poster: Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization »
Hoi-To Wai · Zhuoran Yang · Zhaoran Wang · Mingyi Hong -
2018 Spotlight: Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation »
Qiang Liu · Lihong Li · Ziyang Tang · Denny Zhou -
2018 Poster: A Neural Compositional Paradigm for Image Captioning »
Bo Dai · Sanja Fidler · Dahua Lin -
2018 Poster: On Oracle-Efficient PAC RL with Rich Observations »
Christoph Dann · Nan Jiang · Akshay Krishnamurthy · Alekh Agarwal · John Langford · Robert Schapire -
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: Orthogonally Decoupled Variational Gaussian Processes »
Hugh Salimbeni · Ching-An Cheng · Byron Boots · Marc Deisenroth -
2018 Poster: Learning Safe Policies with Expert Guidance »
Jessie Huang · Fa Wu · Doina Precup · Yang Cai -
2018 Demonstration: Automatic Curriculum Generation Applied to Teaching Novices a Short Bach Piano Segment »
Emma Brunskill · Tong Mu · Karan Goel · Jonathan Bragg -
2018 Spotlight: Efficient Online Portfolio with Logarithmic Regret »
Haipeng Luo · Chen-Yu Wei · Kai Zheng -
2018 Spotlight: On Oracle-Efficient PAC RL with Rich Observations »
Christoph Dann · Nan Jiang · Akshay Krishnamurthy · Alekh Agarwal · John Langford · Robert Schapire -
2018 Poster: Trajectory Convolution for Action Recognition »
Yue Zhao · Yuanjun Xiong · Dahua Lin -
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: Spectral Filtering for General Linear Dynamical Systems »
Elad Hazan · Holden Lee · Karan Singh · Cyril Zhang · Yi Zhang -
2018 Oral: Spectral Filtering for General Linear Dynamical Systems »
Elad Hazan · Holden Lee · Karan Singh · Cyril Zhang · Yi Zhang -
2017 : Poster Session »
David Abel · Nicholas Denis · Maria Eckstein · Ronan Fruit · Karan Goel · Joshua Gruenstein · Anna Harutyunyan · Martin Klissarov · Xiangyu Kong · Aviral Kumar · Saurabh Kumar · Miao Liu · Daniel McNamee · Shayegan Omidshafiei · Silviu Pitis · Paulo Rauber · Melrose Roderick · Tianmin Shu · Yizhou Wang · Shangtong Zhang -
2017 : Panel Discussion »
Matt Botvinick · Emma Brunskill · Marcos Campos · Jan Peters · Doina Precup · David Silver · Josh Tenenbaum · Roy Fox -
2017 : Progress on Deep Reinforcement Learning with Temporal Abstraction (Doina Precup) »
Doina Precup -
2017 : Sample efficiency and off policy hierarchical RL (Emma Brunskill) »
Emma Brunskill -
2017 : Doina Precup »
Doina Precup -
2017 : Spotlights & Poster Session »
David Abel · Nicholas Denis · Maria Eckstein · Ronan Fruit · Karan Goel · Joshua Gruenstein · Anna Harutyunyan · Martin Klissarov · Xiangyu Kong · Aviral Kumar · Saurabh Kumar · Miao Liu · Daniel McNamee · Shayegan Omidshafiei · Silviu Pitis · Paulo Rauber · Melrose Roderick · Tianmin Shu · Yizhou Wang · Shangtong Zhang -
2017 : Model-Agnostic Meta-Learning: Universality, Inductive Bias, and Weak Supervision »
Chelsea Finn -
2017 Workshop: Hierarchical Reinforcement Learning »
Andrew G Barto · Doina Precup · Shie Mannor · Tom Schaul · Roy Fox · Carlos Florensa -
2017 : Panel »
Garth Gibson · Joseph Gonzalez · John Langford · Dawn Song -
2017 : Iterative Collaborative Filtering for Sparse Matrix Estimation »
Christina Lee -
2017 : Emma Brunskill (Stanford) »
Emma Brunskill -
2017 : Invited Talk »
Emma Brunskill -
2017 : John Langford (MSR) on Dreaming Contextual Memory »
John Langford -
2017 Workshop: Nearest Neighbors for Modern Applications with Massive Data: An Age-old Solution with New Challenges »
George H Chen · Devavrat Shah · Christina Lee -
2017 Poster: Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation »
Zhaohan Guo · Philip S. Thomas · Emma Brunskill -
2017 Poster: Off-policy evaluation for slate recommendation »
Adith Swaminathan · Akshay Krishnamurthy · Alekh Agarwal · Miro Dudik · John Langford · Damien Jose · Imed Zitouni -
2017 Poster: Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation »
Christian Borgs · Jennifer Chayes · Christina Lee · Devavrat Shah -
2017 Poster: Improving the Expected Improvement Algorithm »
Chao Qin · Diego Klabjan · Daniel Russo -
2017 Poster: Approximate Supermodularity Bounds for Experimental Design »
Luiz Chamon · Alejandro Ribeiro -
2017 Poster: Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning »
Christoph Dann · Tor Lattimore · Emma Brunskill -
2017 Poster: Action Centered Contextual Bandits »
Kristjan Greenewald · Ambuj Tewari · Susan Murphy · Predag Klasnja -
2017 Poster: Boltzmann Exploration Done Right »
Nicolò Cesa-Bianchi · Claudio Gentile · Gergely Neu · Gabor Lugosi -
2017 Poster: Multi-view Matrix Factorization for Linear Dynamical System Estimation »
Mahdi Karami · Martha White · Dale Schuurmans · Csaba Szepesvari -
2017 Spotlight: Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning »
Christoph Dann · Tor Lattimore · Emma Brunskill -
2017 Oral: Off-policy evaluation for slate recommendation »
Adith Swaminathan · Akshay Krishnamurthy · Alekh Agarwal · Miro Dudik · John Langford · Damien Jose · Imed Zitouni -
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: Contrastive Learning for Image Captioning »
Bo Dai · Dahua Lin -
2017 Poster: Online multiclass boosting »
Young H Jung · Jack Goetz · Ambuj Tewari -
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: Predictive-State Decoders: Encoding the Future into Recurrent Networks »
Arun Venkatraman · Nicholas Rhinehart · Wen Sun · Lerrel Pinto · Martial Hebert · Byron Boots · Kris Kitani · J. Bagnell -
2017 Poster: Variational Inference for Gaussian Process Models with Linear Complexity »
Ching-An Cheng · Byron Boots -
2017 Poster: Fast-Slow Recurrent Neural Networks »
Asier Mujika · Florian Meier · Angelika Steger -
2017 Poster: Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma »
Zhuoran Yang · Krishnakumar Balasubramanian · Zhaoran Wang · Han Liu -
2017 Poster: Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls »
Zeyuan Allen-Zhu · Elad Hazan · Wei Hu · Yuanzhi Li -
2017 Poster: Learning Linear Dynamical Systems via Spectral Filtering »
Elad Hazan · Karan Singh · Cyril Zhang -
2017 Spotlight: Online Learning of Linear Dynamical Systems »
Elad Hazan · Karan Singh · Cyril Zhang -
2017 Spotlight: Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls »
Zeyuan Allen-Zhu · Elad Hazan · Wei Hu · Yuanzhi Li -
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 Poster: First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization »
Aryan Mokhtari · Alejandro Ribeiro -
2017 Poster: Predictive State Recurrent Neural Networks »
Carlton Downey · Ahmed Hefny · Byron Boots · Geoffrey Gordon · Boyue Li -
2017 Tutorial: Geometric Deep Learning on Graphs and Manifolds »
Michael Bronstein · Joan Bruna · arthur szlam · Xavier Bresson · Yann LeCun -
2017 Tutorial: Reinforcement Learning with People »
Emma Brunskill -
2016 : Bert Kappen (Radboud University) »
Hilbert J Kappen -
2016 : Anima Anandkumar »
Anima Anandkumar -
2016 : Chelsea Finn (University of California, Berkeley) »
Chelsea Finn -
2016 Workshop: Deep Learning for Action and Interaction »
Chelsea Finn · Raia Hadsell · David Held · Sergey Levine · Percy Liang -
2016 Workshop: Learning with Tensors: Why Now and How? »
Anima Anandkumar · Rong Ge · Yan Liu · Maximilian Nickel · Qi (Rose) Yu -
2016 : Deep Visual Foresight for Planning Robot Motion »
Chelsea Finn -
2016 : Chelsea Finn »
Chelsea Finn -
2016 Workshop: Nonconvex Optimization for Machine Learning: Theory and Practice »
Hossein Mobahi · Anima Anandkumar · Percy Liang · Stefanie Jegelka · Anna Choromanska -
2016 Workshop: The Future of Interactive Machine Learning »
Kory Mathewson @korymath · Kaushik Subramanian · Mark Ho · Robert Loftin · Joseph L Austerweil · Anna Harutyunyan · Doina Precup · Layla El Asri · Matthew Gombolay · Jerry Zhu · Sonia Chernova · Charles Isbell · Patrick M Pilarski · Weng-Keen Wong · Manuela Veloso · Julie A Shah · Matthew Taylor · Brenna Argall · Michael Littman -
2016 Poster: Optimal Black-Box Reductions Between Optimization Objectives »
Zeyuan Allen-Zhu · Elad Hazan -
2016 Poster: Incremental Variational Sparse Gaussian Process Regression »
Ching-An Cheng · Byron Boots -
2016 Poster: Efficient Second Order Online Learning by Sketching »
Haipeng Luo · Alekh Agarwal · Nicolò Cesa-Bianchi · John Langford -
2016 Poster: Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering »
Dogyoon Song · Christina Lee · Yihua Li · Devavrat Shah -
2016 Poster: Convex Two-Layer Modeling with Latent Structure »
Vignesh Ganapathiraman · Xinhua Zhang · Yaoliang Yu · Junfeng Wen -
2016 Poster: Efficient state-space modularization for planning: theory, behavioral and neural signatures »
Daniel McNamee · Daniel M Wolpert · Mate Lengyel -
2016 Poster: Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy »
Aryan Mokhtari · Hadi Daneshmand · Aurelien Lucchi · Thomas Hofmann · Alejandro Ribeiro -
2016 Poster: A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing »
Ming Lin · Jieping Ye -
2016 Poster: A Credit Assignment Compiler for Joint Prediction »
Kai-Wei Chang · He He · Stephane Ross · Hal Daumé III · John Langford -
2016 Poster: A Non-generative Framework and Convex Relaxations for Unsupervised Learning »
Elad Hazan · Tengyu Ma -
2016 Poster: PAC Reinforcement Learning with Rich Observations »
Akshay Krishnamurthy · Alekh Agarwal · John Langford -
2016 Poster: Unsupervised Learning for Physical Interaction through Video Prediction »
Chelsea Finn · Ian Goodfellow · Sergey Levine -
2016 Poster: The Limits of Learning with Missing Data »
Brian Bullins · Elad Hazan · Tomer Koren -
2016 Poster: More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning »
Xinyang Yi · Zhaoran Wang · Zhuoran Yang · Constantine Caramanis · Han Liu -
2016 Poster: Online and Differentially-Private Tensor Decomposition »
Yining Wang · Anima Anandkumar -
2016 Poster: Search Improves Label for Active Learning »
Alina Beygelzimer · Daniel Hsu · John Langford · Chicheng Zhang -
2016 Poster: Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games »
Sougata Chaudhuri · Ambuj Tewari -
2015 : Opening and Overview »
Anima Anandkumar -
2015 Workshop: Non-convex Optimization for Machine Learning: Theory and Practice »
Anima Anandkumar · Niranjan Uma Naresh · Kamalika Chaudhuri · Percy Liang · Sewoong Oh -
2015 Poster: Online Learning for Adversaries with Memory: Price of Past Mistakes »
Oren Anava · Elad Hazan · Shie Mannor -
2015 Poster: Predtron: A Family of Online Algorithms for General Prediction Problems »
Prateek Jain · Nagarajan Natarajan · Ambuj Tewari -
2015 Poster: Fighting Bandits with a New Kind of Smoothness »
Jacob D Abernethy · Chansoo Lee · Ambuj Tewari -
2015 Poster: Logarithmic Time Online Multiclass prediction »
Anna Choromanska · John Langford -
2015 Poster: Fast and Guaranteed Tensor Decomposition via Sketching »
Yining Wang · Hsiao-Yu Tung · Alexander Smola · Anima Anandkumar -
2015 Poster: Efficient and Parsimonious Agnostic Active Learning »
Tzu-Kuo Huang · Alekh Agarwal · Daniel Hsu · John Langford · Robert Schapire -
2015 Spotlight: Logarithmic Time Online Multiclass prediction »
Anna Choromanska · John Langford -
2015 Spotlight: Fast and Guaranteed Tensor Decomposition via Sketching »
Yining Wang · Hsiao-Yu Tung · Alexander Smola · Anima Anandkumar -
2015 Spotlight: Efficient and Parsimonious Agnostic Active Learning »
Tzu-Kuo Huang · Alekh Agarwal · Daniel Hsu · John Langford · Robert Schapire -
2015 Poster: Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection »
Jie Wang · Jieping Ye -
2015 Spotlight: Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection »
Jie Wang · Jieping Ye -
2015 Poster: HONOR: Hybrid Optimization for NOn-convex Regularized problems »
Pinghua Gong · Jieping Ye -
2015 Poster: Alternating Minimization for Regression Problems with Vector-valued Outputs »
Prateek Jain · Ambuj Tewari -
2015 Poster: Data Generation as Sequential Decision Making »
Philip Bachman · Doina Precup -
2015 Spotlight: Data Generation as Sequential Decision Making »
Philip Bachman · Doina Precup -
2015 Poster: Beyond Convexity: Stochastic Quasi-Convex Optimization »
Elad Hazan · Kfir Y. Levy · Shai Shalev-Shwartz -
2015 Poster: Online Gradient Boosting »
Alina Beygelzimer · Elad Hazan · Satyen Kale · Haipeng Luo -
2015 Poster: Basis refinement strategies for linear value function approximation in MDPs »
Gheorghe Comanici · Doina Precup · Prakash Panangaden -
2014 Workshop: Large-scale reinforcement learning and Markov decision problems »
Benjamin Van Roy · Mohammad Ghavamzadeh · Peter Bartlett · Yasin Abbasi Yadkori · Ambuj Tewari -
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: Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation »
Emily Denton · Wojciech Zaremba · Joan Bruna · Yann LeCun · Rob Fergus -
2014 Poster: Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition »
Hanie Sedghi · Anima Anandkumar · Edmond A Jonckheere -
2014 Poster: Non-convex Robust PCA »
Praneeth Netrapalli · Niranjan Uma Naresh · Sujay Sanghavi · Animashree Anandkumar · Prateek Jain -
2014 Poster: Optimizing Energy Production Using Policy Search and Predictive State Representations »
Yuri Grinberg · Doina Precup · Michel Gendreau -
2014 Poster: Learning to Optimize via Information-Directed Sampling »
Daniel Russo · Benjamin Van Roy -
2014 Poster: Learning with Pseudo-Ensembles »
Philip Bachman · Ouais Alsharif · Doina Precup -
2014 Spotlight: Optimizing Energy Production Using Policy Search and Predictive State Representations »
Yuri Grinberg · Doina Precup · Michel Gendreau -
2014 Spotlight: Non-convex Robust PCA »
Praneeth Netrapalli · Niranjan Uma Naresh · Sujay Sanghavi · Animashree Anandkumar · Prateek Jain -
2014 Poster: On Iterative Hard Thresholding Methods for High-dimensional M-Estimation »
Prateek Jain · Ambuj Tewari · Purushottam Kar -
2013 Workshop: Topic Models: Computation, Application, and Evaluation »
David Mimno · Amr Ahmed · Jordan Boyd-Graber · Ankur Moitra · Hanna Wallach · Alexander Smola · David Blei · Anima Anandkumar -
2013 Workshop: Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games. »
Hilbert J Kappen · Naftali Tishby · Jan Peters · Evangelos Theodorou · David H Wolpert · Pedro Ortega -
2013 Workshop: Extreme Classification: Multi-Class & Multi-Label Learning with Millions of Categories »
Manik Varma · John Langford -
2013 Poster: Learning from Limited Demonstrations »
Beomjoon Kim · Amir-massoud Farahmand · Joelle Pineau · Doina Precup -
2013 Poster: (More) Efficient Reinforcement Learning via Posterior Sampling »
Ian Osband · Daniel Russo · Benjamin Van Roy -
2013 Poster: Bellman Error Based Feature Generation using Random Projections on Sparse Spaces »
Mahdi Milani Fard · Yuri Grinberg · Amir-massoud Farahmand · Joelle Pineau · Doina Precup -
2013 Spotlight: Learning from Limited Demonstrations »
Beomjoon Kim · Amir-massoud Farahmand · Joelle Pineau · Doina Precup -
2013 Poster: Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses »
Harish G Ramaswamy · Shivani Agarwal · Ambuj Tewari -
2013 Poster: Eluder Dimension and the Sample Complexity of Optimistic Exploration »
Daniel Russo · Benjamin Van Roy -
2013 Oral: Eluder Dimension and the Sample Complexity of Optimistic Exploration »
Daniel Russo · Benjamin Van Roy -
2013 Spotlight: Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses »
Harish G Ramaswamy · Shivani Agarwal · Ambuj Tewari -
2013 Poster: Learning with Noisy Labels »
Nagarajan Natarajan · Inderjit Dhillon · Pradeep Ravikumar · Ambuj Tewari -
2013 Poster: Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation »
Dahua Lin -
2013 Poster: Computing the Stationary Distribution Locally »
Christina Lee · Asuman Ozdaglar · Devavrat Shah -
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 -
2012 Poster: Value Pursuit Iteration »
Amir-massoud Farahmand · Doina Precup -
2012 Poster: Coupling Nonparametric Mixtures via Latent Dirichlet Processes »
Dahua Lin · John Fisher III -
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: 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 Poster: Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs »
Anima Anandkumar · Ragupathyraj Valluvan -
2012 Poster: On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization »
Andre S Barreto · Doina Precup · Joelle Pineau -
2012 Poster: Feature Clustering for Accelerating Parallel Coordinate Descent »
Chad Scherrer · Ambuj Tewari · Mahantesh Halappanavar · David Haglin -
2011 Poster: High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions »
Animashree Anandkumar · Vincent Tan · Alan S Willsky -
2011 Oral: High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions »
Animashree Anandkumar · Vincent Tan · Alan S Willsky -
2011 Poster: Greedy Algorithms for Structurally Constrained High Dimensional Problems »
Ambuj Tewari · Pradeep Ravikumar · Inderjit Dhillon -
2011 Poster: On the Universality of Online Mirror Descent »
Nati Srebro · Karthik Sridharan · Ambuj Tewari -
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: Nearest Neighbor based Greedy Coordinate Descent »
Inderjit Dhillon · Pradeep Ravikumar · Ambuj Tewari -
2011 Poster: Online Learning: Stochastic, Constrained, and Smoothed Adversaries »
Sasha Rakhlin · Karthik Sridharan · Ambuj Tewari -
2011 Poster: Orthogonal Matching Pursuit with Replacement »
Prateek Jain · Ambuj Tewari · Inderjit Dhillon -
2011 Poster: Reinforcement Learning using Kernel-Based Stochastic Factorization »
Andre S Barreto · Doina Precup · Joelle Pineau -
2011 Poster: Speedy Q-Learning »
Mohammad Gheshlaghi Azar · Remi Munos · Mohammad Ghavamzadeh · Hilbert J Kappen -
2010 Oral: Construction of Dependent Dirichlet Processes based on Poisson Processes »
Dahua Lin · Eric Grimson · John Fisher III -
2010 Poster: Construction of Dependent Dirichlet Processes based on Poisson Processes »
Dahua Lin · Eric Grimson · John Fisher III -
2010 Oral: Online Learning: Random Averages, Combinatorial Parameters, and Learnability »
Sasha Rakhlin · Karthik Sridharan · Ambuj Tewari -
2010 Poster: Online Learning: Random Averages, Combinatorial Parameters, and Learnability »
Sasha Rakhlin · Karthik Sridharan · Ambuj Tewari -
2010 Poster: Smoothness, Low Noise and Fast Rates »
Nati Srebro · Karthik Sridharan · Ambuj Tewari -
2009 Workshop: Probabilistic Approaches for Control and Robotics »
Marc Deisenroth · Hilbert J Kappen · Emo Todorov · Duy Nguyen-Tuong · Carl Edward Rasmussen · Jan Peters -
2009 Poster: On Stochastic and Worst-case Models for Investing »
Elad Hazan · Satyen Kale -
2009 Oral: On Stochastic and Worst-case Models for Investing »
Elad Hazan · Satyen Kale -
2009 Poster: An Efficient Interior-Point Method for Minimum-Regret Learning in Online Convex Optimization »
Elad Hazan · Nimrod Megiddo -
2009 Spotlight: An Efficient Interior-Point Method for Minimum-Regret Learning in Online Convex Optimization »
Elad Hazan · Nimrod Megiddo -
2009 Poster: Beyond Convexity: Online Submodular Minimization »
Elad Hazan · Satyen Kale -
2009 Poster: Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation »
Hamid R Maei · Csaba Szepesvari · Shalabh Batnaghar · Doina Precup · David Silver · Richard Sutton -
2009 Spotlight: Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation »
Hamid R Maei · Csaba Szepesvari · Shalabh Batnaghar · Doina Precup · David Silver · Richard Sutton -
2008 Poster: On the Generalization Ability of Online Strongly Convex Programming Algorithms »
Sham M Kakade · Ambuj Tewari -
2008 Poster: Bounds on marginal probability distributions »
Joris M Mooij · Hilbert J Kappen -
2008 Spotlight: On the Generalization Ability of Online Strongly Convex Programming Algorithms »
Sham M Kakade · Ambuj Tewari -
2008 Spotlight: Bounds on marginal probability distributions »
Joris M Mooij · Hilbert J Kappen -
2008 Poster: Self-organization using dynamical synapses »
Vicenç Gómez · Andreas Kaltenbrunner · Vicente López · Hilbert J Kappen -
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 -
2007 Oral: Adaptive Online Gradient Descent »
Peter Bartlett · Elad Hazan · Sasha Rakhlin -
2007 Poster: Adaptive Online Gradient Descent »
Peter Bartlett · Elad Hazan · Sasha Rakhlin -
2007 Poster: Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs »
Ambuj Tewari · Peter Bartlett -
2007 Poster: Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria »
Elad Hazan · Satyen Kale -
2006 Poster: Sample Complexity of Policy Search with Known Dynamics »
Peter Bartlett · Ambuj Tewari