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• 2021 : Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning »
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• 2021 Spotlight: Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning »
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• 2021 Spotlight: Robust Predictable Control »
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• 2021 Spotlight: Offline Reinforcement Learning as One Big Sequence Modeling Problem »
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• 2021 Spotlight: Neural Additive Models: Interpretable Machine Learning with Neural Nets »
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• 2021 Spotlight: Pragmatic Image Compression for Human-in-the-Loop Decision-Making »
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• 2021 : Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation »
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• 2021 : Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets »
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• 2021 : Hybrid Imitative Planning with Geometric and Predictive Costs in Offroad Environments »
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• 2021 : Extending the WILDS Benchmark for Unsupervised Adaptation »
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• 2021 : Test Time Robustification of Deep Models via Adaptation and Augmentation »
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• 2021 : Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning »
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• 2021 : Data Sharing without Rewards in Multi-Task Offline Reinforcement Learning »
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• 2021 : Should I Run Offline Reinforcement Learning or Behavioral Cloning? »
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• 2021 : DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization »
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• 2021 : Offline Reinforcement Learning with In-sample Q-Learning »
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• 2021 : C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks »
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• 2021 : Behavior Predictive Representations for Generalization in Reinforcement Learning »
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• 2021 : The Information Geometry of Unsupervised Reinforcement Learning »
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• 2021 : Mismatched No More: Joint Model-Policy Optimization for Model-Based RL »
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• 2021 : Offline Meta-Reinforcement Learning with Online Self-Supervision »
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• 2021 : Hybrid Imitative Planning with Geometric and Predictive Costs in Offroad Environments »
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• 2021 : CoMPS: Continual Meta Policy Search »
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• 2021 : Offline Reinforcement Learning with Implicit Q-Learning »
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• 2021 : TRAIL: Near-Optimal Imitation Learning with Suboptimal Data »
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• 2022 Poster: Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret »
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• 2022 : An Empirical Analysis of the Advantages of Finite vs.~Infinite Width Bayesian Neural Networks »
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• 2022 : A Novel Stochastic Gradient Descent Algorithm for LearningPrincipal Subspaces »
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• 2022 : You Only Live Once: Single-Life Reinforcement Learning »
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• 2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
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• 2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
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• 2022 : Feature-Level Synthesis of Human and ML Insights »
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• 2022 : Multi-step Planning for Automated Hyperparameter Optimization with OptFormer »
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• 2022 : Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks »
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• 2022 : Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes »
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• 2022 : Pre-Training for Robots: Leveraging Diverse Multitask Data via Offline Reinforcement Learning »
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• 2022 : Offline Reinforcement Learning from Heteroskedastic Data Via Support Constraints »
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• 2022 : Skill Acquisition by Instruction Augmentation on Offline Datasets »
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• 2022 : PnP-Nav: Plug-and-Play Policies for Generalizable Visual Navigation Across Robots »
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• 2022 : What Makes a Good Explanation?: A Unified View of Properties of Interpretable ML »
  Varshini Subhash · Zixi Chen · Marton Havasi · Weiwei Pan · Finale Doshi-Velez
• 2022 : Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts »
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• 2022 : Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks »
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• 2022 : Confidence-Conditioned Value Functions for Offline Reinforcement Learning »
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• 2022 : Efficient Deep Reinforcement Learning Requires Regulating Statistical Overfitting »
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• 2022 : Revisiting Bellman Errors for Offline Model Selection »
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• 2022 : Contrastive Example-Based Control »
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• 2022 : Trajectory-based Explainability Framework for Offline RL »
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• 2022 : AMORE: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data »
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• 2022 : Offline Reinforcement Learning for Customizable Visual Navigation »
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• 2022 : A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning »
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• 2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
  Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang
• 2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
  Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang
• 2022 : What Makes a Good Explanation?: A Unified View of Properties of Interpretable ML »
  Zixi Chen · Varshini Subhash · Marton Havasi · Weiwei Pan · Finale Doshi-Velez
• 2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
  Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang
• 2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
  Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang
• 2022 : Revisiting Bellman Errors for Offline Model Selection »
  Joshua Zitovsky · Daniel de Marchi · Rishabh Agarwal · Michael Kosorok
• 2022 : Confidence-Conditioned Value Functions for Offline Reinforcement Learning »
  Joey Hong · Aviral Kumar · Sergey Levine
• 2022 : Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks »
  Jesse Farebrother · Joshua Greaves · Rishabh Agarwal · Charline Le Lan · Ross Goroshin · Pablo Samuel Castro · Marc Bellemare
• 2022 : Efficient Deep Reinforcement Learning Requires Regulating Statistical Overfitting »
  Qiyang Li · Aviral Kumar · Ilya Kostrikov · Sergey Levine
• 2022 : Pre-Training for Robots: Leveraging Diverse Multitask Data via Offline Reinforcement Learning »
  Anikait Singh · Aviral Kumar · Frederik Ebert · Yanlai Yang · Chelsea Finn · Sergey Levine
• 2022 : Offline Reinforcement Learning from Heteroskedastic Data Via Support Constraints »
  Anikait Singh · Aviral Kumar · Quan Vuong · Yevgen Chebotar · Sergey Levine
• 2022 : Adversarial Policies Beat Professional-Level Go AIs »
  Tony Wang · Adam Gleave · Nora Belrose · Tom Tseng · Michael Dennis · Yawen Duan · Viktor Pogrebniak · Joseph Miller · Sergey Levine · Stuart J Russell
• 2022 : Contrastive Example-Based Control »
  Kyle Hatch · Sarthak J Shetty · Benjamin Eysenbach · Tianhe Yu · Rafael Rafailov · Russ Salakhutdinov · Sergey Levine · Chelsea Finn
• 2022 : PnP-Nav: Plug-and-Play Policies for Generalizable Visual Navigation Across Robots »
  Dhruv Shah · Ajay Sridhar · Arjun Bhorkar · Noriaki Hirose · Sergey Levine
• 2022 : Offline Reinforcement Learning for Customizable Visual Navigation »
  Dhruv Shah · Arjun Bhorkar · Hrishit Leen · Ilya Kostrikov · Nicholas Rhinehart · Sergey Levine
• 2022 : Investigating Multi-task Pretraining and Generalization in Reinforcement Learning »
  Adrien Ali Taiga · Rishabh Agarwal · Jesse Farebrother · Aaron Courville · Marc Bellemare
• 2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
  Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang
• 2022 : A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning »
  Benjamin Eysenbach · Matthieu Geist · Russ Salakhutdinov · Sergey Levine
• 2022 : Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective »
  Raj Ghugare · Homanga Bharadhwaj · Benjamin Eysenbach · Sergey Levine · Ruslan Salakhutdinov
• 2022 : (When) Are Contrastive Explanations of Reinforcement Learning Helpful? »
  Sanjana Narayanan · Isaac Lage · Finale Doshi-Velez
• 2022 : Leveraging Human Features at Test-Time »
  Isaac Lage · Sonali Parbhoo · Finale Doshi-Velez
• 2022 : An Empirical Analysis of the Advantages of Finite v.s. Infinite Width Bayesian Neural Networks »
  Jiayu Yao · Yaniv Yacoby · Beau Coker · Weiwei Pan · Finale Doshi-Velez
• 2022 : Adversarial Policies Beat Professional-Level Go AIs »
  Tony Wang · Adam Gleave · Nora Belrose · Tom Tseng · Michael Dennis · Yawen Duan · Viktor Pogrebniak · Joseph Miller · Sergey Levine · Stuart Russell
• 2022 : Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes »
  Aviral Kumar · Rishabh Agarwal · XINYANG GENG · George Tucker · Sergey Levine
• 2022 Spotlight: Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret »
  Jiawei Huang · Li Zhao · Tao Qin · Wei Chen · Nan Jiang · Tie-Yan Liu
• 2022 Spotlight: Lightning Talks 4A-1 »
  Jiawei Huang · Su Jia · Abdurakhmon Sadiev · Ruomin Huang · Yuanyu Wan · Denizalp Goktas · Jiechao Guan · Andrew Li · Wei-Wei Tu · Li Zhao · Amy Greenwald · Jiawei Huang · Dmitry Kovalev · Yong Liu · Wenjie Liu · Peter Richtarik · Lijun Zhang · Zhiwu Lu · R Ravi · Tao Qin · Wei Chen · Hu Ding · Nan Jiang · Tie-Yan Liu
• 2022 : Democratizing RL Research by Reusing Prior Computation »
  Rishabh Agarwal
• 2022 Workshop: Reinforcement Learning for Real Life (RL4RealLife) Workshop »
  Yuxi Li · Emma Brunskill · MINMIN CHEN · Omer Gottesman · Lihong Li · Yao Liu · Zhiwei Tony Qin · Matthew Taylor
• 2022 : Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement »
  Michael Chang · Alyssa L Dayan · Franziska Meier · Tom Griffiths · Sergey Levine · Amy Zhang
• 2022 : What Makes a Good Explanation?: A Unified View of Properties of Interpretable ML »
  Varshini Subhash · Zixi Chen · Marton Havasi · Weiwei Pan · Finale Doshi-Velez
• 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 Poster: Oracle Inequalities for Model Selection in Offline Reinforcement Learning »
  Jonathan N Lee · George Tucker · Ofir Nachum · Bo Dai · Emma Brunskill
• 2022 Poster: MEMO: Test Time Robustness via Adaptation and Augmentation »
  Marvin Zhang · Sergey Levine · Chelsea Finn
• 2022 Poster: Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions »
  Audrey Huang · Nan Jiang
• 2022 Poster: First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization »
  Siddharth Reddy · Sergey Levine · Anca Dragan
• 2022 Poster: Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress »
  Rishabh Agarwal · Max Schwarzer · Pablo Samuel Castro · Aaron Courville · Marc Bellemare
• 2022 Poster: DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning »
  Quan Vuong · Aviral Kumar · Sergey Levine · Yevgen Chebotar
• 2022 Poster: Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits »
  Tong Mu · Yash Chandak · Tatsunori Hashimoto · Emma Brunskill
• 2022 Poster: Addressing Leakage in Concept Bottleneck Models »
  Marton Havasi · Sonali Parbhoo · Finale Doshi-Velez
• 2022 Poster: Interaction-Grounded Learning with Action-Inclusive Feedback »
  Tengyang Xie · Akanksha Saran · Dylan J Foster · Lekan Molu · Ida Momennejad · Nan Jiang · Paul Mineiro · John Langford
• 2022 Poster: Adversarial Unlearning: Reducing Confidence Along Adversarial Directions »
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• 2022 Poster: Mismatched No More: Joint Model-Policy Optimization for Model-Based RL »
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• 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: Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity »
  Abhishek Gupta · Aldo Pacchiano · Yuexiang Zhai · Sham Kakade · Sergey Levine
• 2022 Poster: On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL »
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• 2022 Poster: Distributionally Adaptive Meta Reinforcement Learning »
  Anurag Ajay · Abhishek Gupta · Dibya Ghosh · Sergey Levine · Pulkit Agrawal
• 2022 Poster: Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare »
  Shengpu Tang · Maggie Makar · Michael Sjoding · Finale Doshi-Velez · Jenna Wiens
• 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: You Only Live Once: Single-Life Reinforcement Learning »
  Annie Chen · Archit Sharma · Sergey Levine · Chelsea Finn
• 2022 Poster: Towards Learning Universal Hyperparameter Optimizers with Transformers »
  Yutian Chen · Xingyou Song · Chansoo Lee · Zi Wang · Richard Zhang · David Dohan · Kazuya Kawakami · Greg Kochanski · Arnaud Doucet · Marc'Aurelio Ranzato · Sagi Perel · Nando de Freitas
• 2022 Poster: Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation »
  Michael Chang · Tom Griffiths · Sergey Levine
• 2022 Poster: Data-Driven Offline Decision-Making via Invariant Representation Learning »
  Han Qi · Yi Su · Aviral Kumar · Sergey Levine
• 2022 Poster: Contrastive Learning as Goal-Conditioned Reinforcement Learning »
  Benjamin Eysenbach · Tianjun Zhang · Sergey Levine · Russ Salakhutdinov
• 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: Giving Feedback on Interactive Student Programs with Meta-Exploration »
  Evan Liu · Moritz Stephan · Allen Nie · Chris Piech · Emma Brunskill · Chelsea Finn
• 2022 Poster: Imitating Past Successes can be Very Suboptimal »
  Benjamin Eysenbach · Soumith Udatha · Russ Salakhutdinov · Sergey Levine
• 2021 : Speaker Intro »
  Rishabh Agarwal · Aviral Kumar
• 2021 : Speaker Intro »
  Rishabh Agarwal · Aviral Kumar
• 2021 Workshop: Offline Reinforcement Learning »
  Rishabh Agarwal · Aviral Kumar · George Tucker · Justin Fu · Nan Jiang · Doina Precup · Aviral Kumar
• 2021 : Opening Remarks »
  Rishabh Agarwal · Aviral Kumar
• 2021 : Behavior Predictive Representations for Generalization in Reinforcement Learning »
  Siddhant Agarwal · Aaron Courville · Rishabh Agarwal
• 2021 : LAF | Panel discussion »
  Aaron Snoswell · Jake Goldenfein · Finale Doshi-Velez · Evi Micha · Ivana Dusparic · Jonathan Stray
• 2021 : LAF | The Role of Explanation in RL Legitimacy, Accountability, and Feedback »
  Finale Doshi-Velez
• 2021 : Invited talk #2: Finale Doshi-Velez »
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• 2021 Workshop: Ecological Theory of Reinforcement Learning: How Does Task Design Influence Agent Learning? »
  Manfred Díaz · Hiroki Furuta · Elise van der Pol · Lisa Lee · Shixiang (Shane) Gu · Pablo Samuel Castro · Simon Du · Marc Bellemare · Sergey Levine
• 2021 : Data-Driven Offline Optimization for Architecting Hardware Accelerators »
  Aviral Kumar · Amir Yazdanbakhsh · Milad Hashemi · Kevin Swersky · Sergey Levine
• 2021 : Sergey Levine Talk Q&A »
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• 2021 : Safe RL Debate »
  Sylvia Herbert · Animesh Garg · Emma Brunskill · Aleksandra Faust · Dylan Hadfield-Menell
• 2021 : Opinion Contributed Talk: Sergey Levine »
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• 2021 : Offline Meta-Reinforcement Learning with Online Self-Supervision Q&A »
  Vitchyr Pong · Ashvin Nair · Laura Smith · Catherine Huang · Sergey Levine
• 2021 : Offline Meta-Reinforcement Learning with Online Self-Supervision »
  Vitchyr Pong · Ashvin Nair · Laura Smith · Catherine Huang · Sergey Levine
• 2021 : Offline Meta-Reinforcement Learning with Online Self-Supervision »
  Vitchyr Pong · Ashvin Nair · Laura Smith · Catherine Huang · Sergey Levine
• 2021 : DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization Q&A »
  Aviral Kumar · Rishabh Agarwal · Tengyu Ma · Aaron Courville · George Tucker · Sergey Levine
• 2021 : DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization »
  Aviral Kumar · Rishabh Agarwal · Tengyu Ma · Aaron Courville · George Tucker · Sergey Levine
• 2021 Workshop: Distribution shifts: connecting methods and applications (DistShift) »
  Shiori Sagawa · Pang Wei Koh · Fanny Yang · Hongseok Namkoong · Jiashi Feng · Kate Saenko · Percy Liang · Sarah Bird · Sergey Levine
• 2021 Oral: Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification »
  Ben Eysenbach · Sergey Levine · Russ Salakhutdinov
• 2021 Poster: Play to Grade: Testing Coding Games as Classifying Markov Decision Process »
  Allen Nie · Emma Brunskill · Chris Piech
• 2021 Poster: Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning »
  Siyuan Zhang · Nan Jiang
• 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 Poster: Robust Predictable Control »
  Ben Eysenbach · Russ Salakhutdinov · Sergey Levine
• 2021 Poster: Which Mutual-Information Representation Learning Objectives are Sufficient for Control? »
  Kate Rakelly · Abhishek Gupta · Carlos Florensa · Sergey Levine
• 2021 Poster: COMBO: Conservative Offline Model-Based Policy Optimization »
  Tianhe Yu · Aviral Kumar · Rafael Rafailov · Aravind Rajeswaran · Sergey Levine · Chelsea Finn
• 2021 Poster: Outcome-Driven Reinforcement Learning via Variational Inference »
  Tim G. J. Rudner · Vitchyr Pong · Rowan McAllister · Yarin Gal · Sergey Levine
• 2021 Poster: Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning »
  Andrea Zanette · Martin J Wainwright · Emma Brunskill
• 2021 Poster: Universal Off-Policy Evaluation »
  Yash Chandak · Scott Niekum · Bruno da Silva · Erik Learned-Miller · Emma Brunskill · Philip Thomas
• 2021 Poster: Bayesian Adaptation for Covariate Shift »
  Aurick Zhou · Sergey Levine
• 2021 Poster: Offline Reinforcement Learning as One Big Sequence Modeling Problem »
  Michael Janner · Qiyang Li · Sergey Levine
• 2021 Poster: Pragmatic Image Compression for Human-in-the-Loop Decision-Making »
  Sid Reddy · Anca Dragan · Sergey Levine
• 2021 Poster: Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification »
  Ben Eysenbach · Sergey Levine · Russ Salakhutdinov
• 2021 Oral: Deep Reinforcement Learning at the Edge of the Statistical Precipice »
  Rishabh Agarwal · Max Schwarzer · Pablo Samuel Castro · Aaron Courville · Marc Bellemare
• 2021 Oral: Bellman-consistent Pessimism for Offline Reinforcement Learning »
  Tengyang Xie · Ching-An Cheng · Nan Jiang · Paul Mineiro · Alekh Agarwal
• 2021 Poster: Neural Additive Models: Interpretable Machine Learning with Neural Nets »
  Rishabh Agarwal · Levi Melnick · Nicholas Frosst · Xuezhou Zhang · Ben Lengerich · Rich Caruana · Geoffrey Hinton
• 2021 Poster: Information is Power: Intrinsic Control via Information Capture »
  Nicholas Rhinehart · Jenny Wang · Glen Berseth · John Co-Reyes · Danijar Hafner · Chelsea Finn · Sergey Levine
• 2021 Poster: Conservative Data Sharing for Multi-Task Offline Reinforcement Learning »
  Tianhe Yu · Aviral Kumar · Yevgen Chebotar · Karol Hausman · Sergey Levine · Chelsea Finn
• 2021 Poster: Deep Reinforcement Learning at the Edge of the Statistical Precipice »
  Rishabh Agarwal · Max Schwarzer · Pablo Samuel Castro · Aaron Courville · Marc Bellemare
• 2021 Poster: Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning »
  Kai Wang · Sanket Shah · Haipeng Chen · Andrew Perrault · Finale Doshi-Velez · Milind Tambe
• 2021 Poster: Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning »
  Tengyang Xie · Nan Jiang · Huan Wang · Caiming Xiong · Yu Bai
• 2021 Poster: Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability »
  Dibya Ghosh · Jad Rahme · Aviral Kumar · Amy Zhang · Ryan Adams · Sergey Levine
• 2021 Poster: Design of Experiments for Stochastic Contextual Linear Bandits »
  Andrea Zanette · Kefan Dong · Jonathan N Lee · Emma Brunskill
• 2021 Poster: Autonomous Reinforcement Learning via Subgoal Curricula »
  Archit Sharma · Abhishek Gupta · Sergey Levine · Karol Hausman · Chelsea Finn
• 2021 Poster: Adaptive Risk Minimization: Learning to Adapt to Domain Shift »
  Marvin Zhang · Henrik Marklund · Nikita Dhawan · Abhishek Gupta · Sergey Levine · Chelsea Finn
• 2020 : Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization »
  Brandon Trabucco · Aviral Kumar · XINYANG GENG · Sergey Levine
• 2020 : Conservative Objective Models: A Simple Approach to Effective Model-Based Optimization »
  Brandon Trabucco · Aviral Kumar · XINYANG GENG · Sergey Levine
• 2020 : Batch RL Models Built for Validation »
  Finale Doshi-Velez
• 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 : Towards Reliable Validation and Evaluation for Offline RL »
  Nan Jiang
• 2020 : Contributed Talk #3: Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning »
  Rishabh Agarwal · Marlos C. Machado · Pablo Samuel Castro · Marc Bellemare
• 2020 : Panel »
  Emma Brunskill · Nan Jiang · Nando de Freitas · Finale Doshi-Velez · Sergey Levine · John Langford · Lihong Li · George Tucker · Rishabh Agarwal · Aviral Kumar
• 2020 : Mini-panel discussion 1 - Bridging the gap between theory and practice »
  Aviv Tamar · Emma Brunskill · Jost Tobias Springenberg · Omer Gottesman · Daniel Mankowitz
• 2020 : Q & A and Panel Session with Tom Mitchell, Jenn Wortman Vaughan, Sanjoy Dasgupta, and Finale Doshi-Velez »
  Tom Mitchell · Jennifer Wortman Vaughan · Sanjoy Dasgupta · Finale Doshi-Velez · Zachary Lipton
• 2020 : Keynote: Emma Brunskill »
  Emma Brunskill
• 2020 Workshop: Offline Reinforcement Learning »
  Aviral Kumar · Rishabh Agarwal · George Tucker · Lihong Li · Doina Precup · Aviral Kumar
• 2020 : Offline RL »
  Nando de Freitas
• 2020 : Introduction »
  Aviral Kumar · George Tucker · Rishabh Agarwal
• 2020 Workshop: I Can’t Believe It’s Not Better! Bridging the gap between theory and empiricism in probabilistic machine learning »
  Jessica Forde · Francisco Ruiz · Melanie Fernandez Pradier · Aaron Schein · Finale Doshi-Velez · Isabel Valera · David Blei · Hanna Wallach
• 2020 : Contributed Talk: MaxEnt RL and Robust Control »
  Benjamin Eysenbach · Sergey Levine
• 2020 : Panel discussion on minimizing bias in machine learning in education »
  Neil Heffernan · Osonde A. Osoba · Emma Brunskill · Kathi Fisler
• 2020 Poster: Model Inversion Networks for Model-Based Optimization »
  Aviral Kumar · Sergey Levine
• 2020 Poster: Incorporating Interpretable Output Constraints in Bayesian Neural Networks »
  Wanqian Yang · Lars Lorch · Moritz Graule · Himabindu Lakkaraju · Finale Doshi-Velez
• 2020 Spotlight: Incorporating Interpretable Output Constraints in Bayesian Neural Networks »
  Wanqian Yang · Lars Lorch · Moritz Graule · Himabindu Lakkaraju · Finale Doshi-Velez
• 2020 Poster: Continual Learning of Control Primitives : Skill Discovery via Reset-Games »
  Kelvin Xu · Siddharth Verma · Chelsea Finn · Sergey Levine
• 2020 Poster: Critic Regularized Regression »
  Ziyu Wang · Alexander Novikov · Konrad Zolna · Josh Merel · Jost Tobias Springenberg · Scott Reed · Bobak Shahriari · Noah Siegel · Caglar Gulcehre · Nicolas Heess · Nando de Freitas
• 2020 Poster: Gradient Surgery for Multi-Task Learning »
  Tianhe Yu · Saurabh Kumar · Abhishek Gupta · Sergey Levine · Karol Hausman · Chelsea Finn
• 2020 Poster: Modular Meta-Learning with Shrinkage »
  Yutian Chen · Abram Friesen · Feryal Behbahani · Arnaud Doucet · David Budden · Matthew Hoffman · Nando de Freitas
• 2020 Spotlight: Modular Meta-Learning with Shrinkage »
  Yutian Chen · Abram Friesen · Feryal Behbahani · Arnaud Doucet · David Budden · Matthew Hoffman · Nando de Freitas
• 2020 Poster: RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning »
  Caglar Gulcehre · Ziyu Wang · Alexander Novikov · Thomas Paine · Sergio Gómez · Konrad Zolna · Rishabh Agarwal · Josh Merel · Daniel Mankowitz · Cosmin Paduraru · Gabriel Dulac-Arnold · Jerry Li · Mohammad Norouzi · Matthew Hoffman · Nicolas Heess · Nando de Freitas
• 2020 Poster: Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement »
  Benjamin Eysenbach · XINYANG GENG · Sergey Levine · Russ Salakhutdinov
• 2020 Poster: Conservative Q-Learning for Offline Reinforcement Learning »
  Aviral Kumar · Aurick Zhou · George Tucker · Sergey Levine
• 2020 Oral: Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement »
  Benjamin Eysenbach · XINYANG GENG · Sergey Levine · Russ Salakhutdinov
• 2020 Tutorial: (Track3) Offline Reinforcement Learning: From Algorithm Design to Practical Applications Q&A »
  Sergey Levine · Aviral Kumar
• 2020 Poster: Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction »
  Michael Janner · Igor Mordatch · Sergey Levine
• 2020 Poster: Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding »
  Hongseok Namkoong · Ramtin Keramati · Steve Yadlowsky · Emma Brunskill
• 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: Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors »
  Karl Pertsch · Oleh Rybkin · Frederik Ebert · Shenghao Zhou · Dinesh Jayaraman · Chelsea Finn · Sergey Levine
• 2020 Poster: Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model »
  Alex X. Lee · Anusha Nagabandi · Pieter Abbeel · Sergey Levine
• 2020 Poster: Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration »
  Andrea Zanette · Alessandro Lazaric · Mykel J Kochenderfer · Emma Brunskill
• 2020 Poster: Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs »
  Jianzhun Du · Joseph Futoma · Finale Doshi-Velez
• 2020 Poster: Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design »
  Michael Dennis · Natasha Jaques · Eugene Vinitsky · Alexandre Bayen · Stuart Russell · Andrew Critch · Sergey Levine
• 2020 Poster: MOPO: Model-based Offline Policy Optimization »
  Tianhe Yu · Garrett Thomas · Lantao Yu · Stefano Ermon · James Zou · Sergey Levine · Chelsea Finn · Tengyu Ma
• 2020 Poster: DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction »
  Aviral Kumar · Abhishek Gupta · Sergey Levine
• 2020 Poster: Provably Good Batch Reinforcement Learning Without Great Exploration »
  Yao Liu · Adith Swaminathan · Alekh Agarwal · Emma Brunskill
• 2020 Spotlight: DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction »
  Aviral Kumar · Abhishek Gupta · Sergey Levine
• 2020 Oral: Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design »
  Michael Dennis · Natasha Jaques · Eugene Vinitsky · Alexandre Bayen · Stuart Russell · Andrew Critch · Sergey Levine
• 2020 : Discussion Panel: Hugo Larochelle, Finale Doshi-Velez, Devi Parikh, Marc Deisenroth, Julien Mairal, Katja Hofmann, Phillip Isola, and Michael Bowling »
  Hugo Larochelle · Finale Doshi-Velez · Marc Deisenroth · Devi Parikh · Julien Mairal · Katja Hofmann · Phillip Isola · Michael Bowling
• 2020 Tutorial: (Track3) Offline Reinforcement Learning: From Algorithm Design to Practical Applications »
  Sergey Levine · Aviral Kumar
• 2019 : Emma Brünskill, "Some Theory RL Challenges Inspired by Education" »
  Emma Brunskill
• 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 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 : Contributed Talks »
  Rishabh Agarwal · Adam Gleave · Kimin Lee
• 2019 : Invited Talk »
  Emma Brunskill
• 2019 : Poster and Coffee Break 1 »
  Aaron Sidford · Aditya Mahajan · Alejandro Ribeiro · Alex Lewandowski · Ali H Sayed · Ambuj Tewari · Angelika Steger · Anima Anandkumar · Asier Mujika · Hilbert J Kappen · Bolei Zhou · Byron Boots · Chelsea Finn · Chen-Yu Wei · Chi Jin · Ching-An Cheng · Christina Yu · Clement Gehring · Craig Boutilier · Dahua Lin · Daniel McNamee · Daniel Russo · David Brandfonbrener · Denny Zhou · Devesh Jha · Diego Romeres · Doina Precup · Dominik Thalmeier · Eduard Gorbunov · Elad Hazan · Elena Smirnova · Elvis Dohmatob · Emma Brunskill · Enrique Munoz de Cote · Ethan Waldie · Florian Meier · Florian Schaefer · Ge Liu · Gergely Neu · Haim Kaplan · Hao Sun · Hengshuai Yao · Jalaj Bhandari · James A Preiss · Jayakumar Subramanian · Jiajin Li · Jieping Ye · Jimmy Smith · Joan Bas Serrano · Joan Bruna · John Langford · Jonathan Lee · Jose A. Arjona-Medina · Kaiqing Zhang · Karan Singh · Yuping Luo · Zafarali Ahmed · Zaiwei Chen · Zhaoran Wang · Zhizhong Li · Zhuoran Yang · Ziping Xu · Ziyang Tang · Yi Mao · David Brandfonbrener · Shirli Di-Castro · Riashat Islam · Zuyue Fu · Abhishek Naik · Saurabh Kumar · Benjamin Petit · Angeliki Kamoutsi · Simone Totaro · Arvind Raghunathan · Rui Wu · Donghwan Lee · Dongsheng Ding · Alec Koppel · Hao Sun · Christian Tjandraatmadja · Mahdi Karami · Jincheng Mei · Chenjun Xiao · Junfeng Wen · Zichen Zhang · Ross Goroshin · Mohammad Pezeshki · Jiaqi Zhai · Philip Amortila · Shuo Huang · Mariya Vasileva · El houcine Bergou · Adel Ahmadyan · Haoran Sun · Sheng Zhang · Lukas Gruber · Yuanhao Wang · Tetiana Parshakova
• 2019 Workshop: Science meets Engineering of Deep Learning »
  Levent Sagun · Caglar Gulcehre · Adriana Romero Soriano · Negar Rostamzadeh · Nando de Freitas
• 2019 : Welcoming remarks and introduction »
  Levent Sagun · Caglar Gulcehre · Adriana Romero Soriano · Negar Rostamzadeh · Nando de Freitas
• 2019 : Invited talk #4 »
  Finale Doshi-Velez
• 2019 : Poster Session »
  Ethan Harris · Tom White · Oh Hyeon Choung · Takashi Shinozaki · Dipan Pal · Katherine L. Hermann · Judy Borowski · Camilo Fosco · Chaz Firestone · Vijay Veerabadran · Benjamin Lahner · Chaitanya Ryali · Fenil Doshi · Pulkit Singh · Sharon Zhou · Michel Besserve · Michael Chang · Anelise Newman · Mahesan Niranjan · Jonathon Hare · Daniela Mihai · Marios Savvides · Simon Kornblith · Christina M Funke · Aude Oliva · Virginia de Sa · Dmitry Krotov · Colin Conwell · George Alvarez · Alex Kolchinski · Shengjia Zhao · Mitchell Gordon · Michael Bernstein · Stefano Ermon · Arash Mehrjou · Bernhard Schölkopf · John Co-Reyes · Michael Janner · Jiajun Wu · Josh Tenenbaum · Sergey Levine · Yalda Mohsenzadeh · Zhenglong Zhou
• 2019 : Finale Doshi-Velez: Combining Statistical methods with Human Input for Evaluation and Optimization in Batch Settings »
  Finale Doshi-Velez
• 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 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: Wasserstein Dependency Measure for Representation Learning »
  Sherjil Ozair · Corey Lynch · Yoshua Bengio · Aaron van den Oord · Sergey Levine · Pierre Sermanet
• 2019 Poster: Planning with Goal-Conditioned Policies »
  Soroush Nasiriany · Vitchyr Pong · Steven Lin · Sergey Levine
• 2019 Poster: Search on the Replay Buffer: Bridging Planning and Reinforcement Learning »
  Benjamin Eysenbach · Russ Salakhutdinov · Sergey Levine
• 2019 Poster: MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies »
  Xue Bin Peng · Michael Chang · Grace Zhang · Pieter Abbeel · Sergey Levine
• 2019 Poster: Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction »
  Aviral Kumar · Justin Fu · George Tucker · Sergey Levine
• 2019 Poster: Unsupervised Curricula for Visual Meta-Reinforcement Learning »
  Allan Jabri · Kyle Hsu · Abhishek Gupta · Benjamin Eysenbach · Sergey Levine · Chelsea Finn
• 2019 Poster: Compositional Plan Vectors »
  Coline Devin · Daniel Geng · Pieter Abbeel · Trevor Darrell · Sergey Levine
• 2019 Spotlight: Unsupervised Curricula for Visual Meta-Reinforcement Learning »
  Allan Jabri · Kyle Hsu · Abhishek Gupta · Benjamin Eysenbach · Sergey Levine · Chelsea Finn
• 2019 Poster: Causal Confusion in Imitation Learning »
  Pim de Haan · Dinesh Jayaraman · Sergey Levine
• 2019 Poster: Meta-Learning with Implicit Gradients »
  Aravind Rajeswaran · Chelsea Finn · Sham Kakade · Sergey Levine
• 2019 Poster: Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model »
  Andrea Zanette · Mykel J Kochenderfer · Emma Brunskill
• 2019 Poster: When to Trust Your Model: Model-Based Policy Optimization »
  Michael Janner · Justin Fu · Marvin Zhang · Sergey Levine
• 2019 Poster: Guided Meta-Policy Search »
  Russell Mendonca · Abhishek Gupta · Rosen Kralev · Pieter Abbeel · Sergey Levine · Chelsea Finn
• 2019 Poster: Learning Compositional Neural Programs with Recursive Tree Search and Planning »
  Thomas PIERROT · Guillaume Ligner · Scott Reed · Olivier Sigaud · Nicolas Perrin · Alexandre Laterre · David Kas · Karim Beguir · Nando de Freitas
• 2019 Spotlight: Guided Meta-Policy Search »
  Russell Mendonca · Abhishek Gupta · Rosen Kralev · Pieter Abbeel · Sergey Levine · Chelsea Finn
• 2019 Spotlight: Learning Compositional Neural Programs with Recursive Tree Search and Planning »
  Thomas PIERROT · Guillaume Ligner · Scott Reed · Olivier Sigaud · Nicolas Perrin · Alexandre Laterre · David Kas · Karim Beguir · Nando de Freitas
• 2019 Oral: Causal Confusion in Imitation Learning »
  Pim de Haan · Dinesh Jayaraman · Sergey Levine
• 2019 Poster: Limiting Extrapolation in Linear Approximate Value Iteration »
  Andrea Zanette · Alessandro Lazaric · Mykel J Kochenderfer · Emma Brunskill
• 2019 Poster: Provably Efficient Q-Learning with Low Switching Cost »
  Yu Bai · Tengyang Xie · Nan Jiang · Yu-Xiang Wang
• 2018 : TBA 5 »
  Nando de Freitas
• 2018 : Invited Talk 5: Nando de Freitas »
  Nando de Freitas
• 2018 : Meta-Learning to Follow Instructions, Examples, and Demonstrations »
  Sergey Levine
• 2018 : TBA 2 »
  Sergey Levine
• 2018 : Finale Doshi-Velez »
  Finale Doshi-Velez
• 2018 : Control as Inference and Soft Deep RL (Sergey Levine) »
  Sergey Levine
• 2018 : Panel on research process »
  Zachary Lipton · Charles Sutton · Finale Doshi-Velez · Hanna Wallach · Suchi Saria · Rich Caruana · Thomas Rainforth
• 2018 : TBC 9 »
  Sergey Levine
• 2018 : Finale Doshi-Velez »
  Finale Doshi-Velez
• 2018 Poster: Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models »
  Kurtland Chua · Roberto Calandra · Rowan McAllister · Sergey Levine
• 2018 Spotlight: Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models »
  Kurtland Chua · Roberto Calandra · Rowan McAllister · Sergey Levine
• 2018 Poster: Probabilistic Model-Agnostic Meta-Learning »
  Chelsea Finn · Kelvin Xu · Sergey Levine
• 2018 Poster: Human-in-the-Loop Interpretability Prior »
  Isaac Lage · Andrew Ross · Samuel J Gershman · Been Kim · Finale Doshi-Velez
• 2018 Spotlight: Human-in-the-Loop Interpretability Prior »
  Isaac Lage · Andrew Ross · Samuel J Gershman · Been Kim · Finale Doshi-Velez
• 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: Playing hard exploration games by watching YouTube »
  Yusuf Aytar · Tobias Pfaff · David Budden · Thomas Paine · Ziyu Wang · Nando de Freitas
• 2018 Poster: Meta-Reinforcement Learning of Structured Exploration Strategies »
  Abhishek Gupta · Russell Mendonca · YuXuan Liu · Pieter Abbeel · Sergey Levine
• 2018 Poster: Visual Reinforcement Learning with Imagined Goals »
  Ashvin Nair · Vitchyr Pong · Murtaza Dalal · Shikhar Bahl · Steven Lin · Sergey Levine
• 2018 Spotlight: Playing hard exploration games by watching YouTube »
  Yusuf Aytar · Tobias Pfaff · David Budden · Thomas Paine · Ziyu Wang · Nando de Freitas
• 2018 Spotlight: Visual Reinforcement Learning with Imagined Goals »
  Ashvin Nair · Vitchyr Pong · Murtaza Dalal · Shikhar Bahl · Steven Lin · Sergey Levine
• 2018 Spotlight: Meta-Reinforcement Learning of Structured Exploration Strategies »
  Abhishek Gupta · Russell Mendonca · YuXuan Liu · Pieter Abbeel · Sergey Levine
• 2018 Poster: Visual Memory for Robust Path Following »
  Ashish Kumar · Saurabh Gupta · David Fouhey · Sergey Levine · Jitendra Malik
• 2018 Poster: Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition »
  Justin Fu · Avi Singh · Dibya Ghosh · Larry Yang · Sergey Levine
• 2018 Demonstration: Automatic Curriculum Generation Applied to Teaching Novices a Short Bach Piano Segment »
  Emma Brunskill · Tong Mu · Karan Goel · Jonathan Bragg
• 2018 Oral: Visual Memory for Robust Path Following »
  Ashish Kumar · Saurabh Gupta · David Fouhey · Sergey Levine · Jitendra Malik
• 2018 Poster: Data-Efficient Hierarchical Reinforcement Learning »
  Ofir Nachum · Shixiang (Shane) Gu · Honglak Lee · Sergey Levine
• 2018 Poster: Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior »
  Sid Reddy · Anca Dragan · Sergey Levine
• 2017 : Panel Discussion »
  Matt Botvinick · Emma Brunskill · Marcos Campos · Jan Peters · Doina Precup · David Silver · Josh Tenenbaum · Roy Fox
• 2017 : Panel Session »
  Neil Lawrence · Finale Doshi-Velez · Zoubin Ghahramani · Yann LeCun · Max Welling · Yee Whye Teh · Ole Winther
• 2017 : Sample efficiency and off policy hierarchical RL (Emma Brunskill) »
  Emma Brunskill
• 2017 : Finale Doshi-Velez »
  Finale Doshi-Velez
• 2017 : Automatic Model Selection in BNNs with Horseshoe Priors »
  Finale Doshi-Velez
• 2017 Workshop: Workshop on Meta-Learning »
  Roberto Calandra · Frank Hutter · Hugo Larochelle · Sergey Levine
• 2017 : Emma Brunskill (Stanford) »
  Emma Brunskill
• 2017 : Coffee break and Poster Session I »
  Nishith Khandwala · Steve Gallant · Gregory Way · Aniruddh Raghu · Li Shen · Aydan Gasimova · Alican Bozkurt · William Boag · Daniel Lopez-Martinez · Ulrich Bodenhofer · Samaneh Nasiri GhoshehBolagh · Michelle Guo · Christoph Kurz · Kirubin Pillay · Kimis Perros · George H Chen · Alexandre Yahi · Madhumita Sushil · Sanjay Purushotham · Elena Tutubalina · Tejpal Virdi · Marc-Andre Schulz · Samuel Weisenthal · Bharat Srikishan · Petar Veličković · Kartik Ahuja · Andrew Miller · Erin Craig · Disi Ji · Filip Dabek · Chloé Pou-Prom · Hejia Zhang · Janani Kalyanam · Wei-Hung Weng · Harish Bhat · Hugh Chen · Simon Kohl · Mingwu Gao · Tingting Zhu · Ming-Zher Poh · Iñigo Urteaga · Antoine Honoré · Alessandro De Palma · Maruan Al-Shedivat · Pranav Rajpurkar · Matthew McDermott · Vincent Chen · Yanan Sui · Yun-Geun Lee · Li-Fang Cheng · Chen Fang · Sibt ul Hussain · Cesare Furlanello · Zeev Waks · Hiba Chougrad · Hedvig Kjellstrom · Finale Doshi-Velez · Wolfgang Fruehwirt · Yanqing Zhang · Lily Hu · Junfang Chen · Sunho Park · Gatis Mikelsons · Jumana Dakka · Stephanie Hyland · yann chevaleyre · Hyunwoo Lee · Xavier Giro-i-Nieto · David Kale · Michael Hughes · Gabriel Erion · Rishab Mehra · William Zame · Stojan Trajanovski · Prithwish Chakraborty · Kelly Peterson · Muktabh Mayank Srivastava · Amy Jin · Heliodoro Tejeda Lemus · Priyadip Ray · Tamas Madl · Joseph Futoma · Enhao Gong · Syed Rameel Ahmad · Eric Lei · Ferdinand Legros
• 2017 : Contributed talk: Beyond Sparsity: Tree-based Regularization of Deep Models for Interpretability »
  Mike Wu · Sonali Parbhoo · Finale Doshi-Velez
• 2017 : Invited talk: The Role of Explanation in Holding AIs Accountable »
  Finale Doshi-Velez
• 2017 : Invited Talk »
  Emma Brunskill
• 2017 Poster: Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation »
  Zhaohan Guo · Philip S. Thomas · Emma Brunskill
• 2017 Poster: EX2: Exploration with Exemplar Models for Deep Reinforcement Learning »
  Justin Fu · John Co-Reyes · Sergey Levine
• 2017 Poster: Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning »
  Christoph Dann · Tor Lattimore · Emma Brunskill
• 2017 Poster: Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes »
  Taylor Killian · Samuel Daulton · Finale Doshi-Velez · George Konidaris
• 2017 Spotlight: EX2: Exploration with Exemplar Models for Deep Reinforcement Learning »
  Justin Fu · John Co-Reyes · Sergey Levine
• 2017 Spotlight: Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning »
  Christoph Dann · Tor Lattimore · Emma Brunskill
• 2017 Oral: Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes »
  Taylor Killian · Samuel Daulton · Finale Doshi-Velez · George Konidaris
• 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: Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning »
  Shixiang (Shane) Gu · Timothy Lillicrap · Richard Turner · Zoubin Ghahramani · Bernhard Schölkopf · Sergey Levine
• 2017 Poster: Robust Imitation of Diverse Behaviors »
  Ziyu Wang · Josh Merel · Scott Reed · Nando de Freitas · Gregory Wayne · Nicolas Heess
• 2017 Tutorial: Deep Learning: Practice and Trends »
  Nando de Freitas · Scott Reed · Oriol Vinyals
• 2017 Tutorial: Reinforcement Learning with People »
  Emma Brunskill
• 2016 : BNNs for RL: A Success Story and Open Questions »
  Finale Doshi-Velez
• 2016 Workshop: Deep Learning for Action and Interaction »
  Chelsea Finn · Raia Hadsell · David Held · Sergey Levine · Percy Liang
• 2016 Workshop: Neural Abstract Machines & Program Induction »
  Matko Bošnjak · Nando de Freitas · Tejas Kulkarni · Arvind Neelakantan · Scott E Reed · Sebastian Riedel · Tim Rocktäschel
• 2016 : Sergey Levine (University of California, Berkeley) »
  Sergey Levine
• 2016 : Nando De Freitas »
  Nando de Freitas
• 2016 : Learning To Optimize »
  Nando de Freitas
• 2016 Poster: Learning to learn by gradient descent by gradient descent »
  Marcin Andrychowicz · Misha Denil · Sergio Gómez · Matthew Hoffman · David Pfau · Tom Schaul · Nando de Freitas
• 2016 Poster: Value Iteration Networks »
  Aviv Tamar · Sergey Levine · Pieter Abbeel · YI WU · Garrett Thomas
• 2016 Oral: Value Iteration Networks »
  Aviv Tamar · Sergey Levine · Pieter Abbeel · YI WU · Garrett Thomas
• 2015 Workshop: Bayesian Optimization: Scalability and Flexibility »
  Bobak Shahriari · Ryan Adams · Nando de Freitas · Amar Shah · Roberto Calandra
• 2015 Workshop: Machine Learning From and For Adaptive User Technologies: From Active Learning & Experimentation to Optimization & Personalization »
  Joseph Jay Williams · Yasin Abbasi Yadkori · Finale Doshi-Velez
• 2015 : Deep Robotic Learning »
  Sergey Levine
• 2015 : Data Driven Phenotyping for Diseases »
  Finale Doshi-Velez
• 2015 Poster: Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction »
  Been Kim · Julie A Shah · Finale Doshi-Velez
• 2014 Workshop: Novel Trends and Applications in Reinforcement Learning »
  Csaba Szepesvari · Marc Deisenroth · Sergey Levine · Pedro Ortega · Brian Ziebart · Emma Brunskill · Naftali Tishby · Gerhard Neumann · Daniel Lee · Sridhar Mahadevan · Pieter Abbeel · David Silver · Vicenç Gómez
• 2014 Poster: Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics »
  Sergey Levine · Pieter Abbeel
• 2014 Spotlight: Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics »
  Sergey Levine · Pieter Abbeel
• 2013 Poster: Variational Policy Search via Trajectory Optimization »
  Sergey Levine · Vladlen Koltun
• 2010 Poster: Feature Construction for Inverse Reinforcement Learning »
  Sergey Levine · Zoran Popovic · Vladlen Koltun