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Spotlight
Zhiwei (Tony) Qin · Xianyuan Zhan · Meng Qi · Ruihan Yang · Philip Ball · Hamsa Bastani · Yao Liu · Xiuwen Wang · Haoran Xu · Tony Z. Zhao · Lili Chen · Aviral Kumar
Author Information
Zhiwei (Tony) Qin (Didi Research America)
Xianyuan Zhan (Tsinghua University)
Meng Qi (University of California, Berkeley)
Ruihan Yang (UC San Diego)
Philip Ball (University of Oxford)
Hamsa Bastani (Wharton)
Yao Liu (Stanford University)
Xiuwen Wang (The Chinese University of Hong Kong, Shenzhen)
Haoran Xu (Xidian University)
Tony Z. Zhao (UC Berkeley)
Lili Chen (UC Berkeley)
Aviral Kumar (UC Berkeley)
More from the Same Authors
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2020 : One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL »
Saurabh Kumar · Aviral Kumar -
2021 : Improving Human Decision-Making with Machine Learning »
Hamsa Bastani -
2021 : Robust Generalization of Quadratic Neural Networks via Function Identification »
Kan Xu · Hamsa Bastani · Osbert Bastani -
2021 : Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability »
Dibya Ghosh · Jad Rahme · Aviral Kumar · Amy Zhang · Ryan P. Adams · Sergey Levine -
2021 : Provably efficient exploration-free transfer RL for near-deterministic latent dynamics »
Yao Liu · Dipendra Misra · Miroslav Dudik · Robert Schapire -
2021 : Mind the Gap: Safely Bridging Offline and Online Reinforcement Learning »
Wanqiao Xu · Kan Xu · Hamsa Bastani · Osbert Bastani -
2021 : Value-Based Deep Reinforcement Learning Requires Explicit Regularization »
Aviral Kumar · Rishabh Agarwal · Aaron Courville · Tengyu Ma · George Tucker · Sergey Levine -
2021 : Multi-Task Offline Reinforcement Learning with Conservative Data Sharing »
Tianhe (Kevin) Yu · Aviral Kumar · Yevgen Chebotar · Karol Hausman · Sergey Levine · Chelsea Finn -
2021 : DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning »
Xianyuan Zhan · Haoran Xu · Yue Zhang · Xiangyu Zhu · Honglei Yin -
2021 : Reinforcement Learning for (Mixed) Integer Programming: Smart Feasibility Pump »
Mengxin Wang · Meng Qi · Zuo-Jun Shen -
2021 : Learning Vision-Guided Quadrupedal Locomotionwith Cross-Modal Transformers »
Ruihan Yang · Minghao Zhang · Nicklas Hansen · Harry (Huazhe) Xu · Xiaolong Wang -
2021 : Revisiting Design Choices in Offline Model Based Reinforcement Learning »
Cong Lu · Philip Ball · Jack Parker-Holder · Michael A Osborne · Stephen Roberts -
2021 : Mind the Gap: Safely Bridging Offline and Online Reinforcement Learning »
Wanqiao Xu · Kan Xu · Hamsa Bastani · Osbert Bastani -
2021 : Deploying a Machine Learning System for COVID-19 Testing in Greece »
Hamsa Bastani · Kimon Drakopoulos · Vishal Gupta -
2021 : Improving Human Decision-Making with Machine Learning »
Hamsa Bastani · Osbert Bastani · Wichinpong Sinchaisri -
2021 : Avoiding Overfitting to the Importance Weights in Offline Policy Optimization »
Yao Liu · Emma Brunskill -
2021 : Constraints Penalized Q-Learning for Safe Offline Reinforcement Learning »
Haoran Xu · Xianyuan Zhan · Xiangyu Zhu -
2021 : Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention »
Abhishek Gupta · Justin Yu · Tony Z. Zhao · Vikash Kumar · Aaron Rovinsky · Kelvin Xu · Thomas Devlin · Sergey Levine -
2021 : Decision Transformer: Reinforcement Learning via Sequence Modeling »
Lili Chen · Kevin Lu · Aravind Rajeswaran · Kimin Lee · Aditya Grover · Michael Laskin · Pieter Abbeel · Aravind Srinivas · Igor Mordatch -
2021 : Multi-Task Offline Reinforcement Learning with Conservative Data Sharing »
Tianhe (Kevin) Yu · Aviral Kumar · Yevgen Chebotar · Karol Hausman · Sergey Levine · Chelsea Finn -
2021 : Value-Based Deep Reinforcement Learning Requires Explicit Regularization »
Aviral Kumar · Rishabh Agarwal · Aaron Courville · Tengyu Ma · George Tucker · Sergey Levine -
2021 : Value-Based Deep Reinforcement Learning Requires Explicit Regularization »
Aviral Kumar · Rishabh Agarwal · Aaron Courville · Tengyu Ma · George Tucker · Sergey Levine -
2021 : Improving Human Decision-Making with Machine Learning »
Hamsa Bastani · Osbert Bastani · Wichinpong Sinchaisri -
2022 : Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations »
Cong Lu · Philip Ball · Tim G. J Rudner · Jack Parker-Holder · Michael A Osborne · Yee-Whye Teh -
2022 Poster: Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization »
Brandon Trabucco · Xinyang Geng · Aviral Kumar · Sergey Levine -
2022 Poster: How to Leverage Unlabeled Data in Offline Reinforcement Learning »
Tianhe (Kevin) Yu · Aviral Kumar · Yevgen Chebotar · Karol Hausman · Chelsea Finn · Sergey Levine -
2022 Poster: Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations »
Haoran Xu · Xianyuan Zhan · Honglei Yin · Huiling qin -
2022 Spotlight: How to Leverage Unlabeled Data in Offline Reinforcement Learning »
Tianhe (Kevin) Yu · Aviral Kumar · Yevgen Chebotar · Karol Hausman · Chelsea Finn · Sergey Levine -
2022 Spotlight: Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization »
Brandon Trabucco · Xinyang Geng · Aviral Kumar · Sergey Levine -
2022 Spotlight: Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations »
Haoran Xu · Xianyuan Zhan · Honglei Yin · Huiling qin -
2022 Poster: Stabilizing Off-Policy Deep Reinforcement Learning from Pixels »
Edoardo Cetin · Philip Ball · Stephen Roberts · Oya Celiktutan -
2022 Spotlight: Stabilizing Off-Policy Deep Reinforcement Learning from Pixels »
Edoardo Cetin · Philip Ball · Stephen Roberts · Oya Celiktutan -
2021 : Value-Based Deep Reinforcement Learning Requires Explicit Regularization »
Aviral Kumar · Rishabh Agarwal · Aaron Courville · Tengyu Ma · George Tucker · Sergey Levine -
2021 : RL + Operations Research Panel »
Jim Dai · Fei Fang · Shie Mannor · Yuandong Tian · Zhiwei (Tony) Qin · Zongqing Lu -
2021 Workshop: Reinforcement Learning for Real Life »
Yuxi Li · Minmin Chen · Omer Gottesman · Lihong Li · Zongqing Lu · Rupam Mahmood · Niranjani Prasad · Zhiwei (Tony) Qin · Csaba Szepesvari · Matthew Taylor -
2021 Poster: Calibrate Before Use: Improving Few-shot Performance of Language Models »
Tony Z. Zhao · Eric Wallace · Shi Feng · Dan Klein · Sameer Singh -
2021 Oral: Calibrate Before Use: Improving Few-shot Performance of Language Models »
Tony Z. Zhao · Eric Wallace · Shi Feng · Dan Klein · Sameer Singh -
2021 Poster: Conservative Objective Models for Effective Offline Model-Based Optimization »
Brandon Trabucco · Aviral Kumar · Xinyang Geng · Sergey Levine -
2021 Spotlight: Conservative Objective Models for Effective Offline Model-Based Optimization »
Brandon Trabucco · Aviral Kumar · Xinyang Geng · Sergey Levine -
2021 Poster: Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings »
Kan Xu · Xuanyi Zhao · Hamsa Bastani · Osbert Bastani -
2021 Spotlight: Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings »
Kan Xu · Xuanyi Zhao · Hamsa Bastani · Osbert Bastani -
2021 Poster: Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment »
Philip Ball · Cong Lu · Jack Parker-Holder · Stephen Roberts -
2021 Spotlight: Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment »
Philip Ball · Cong Lu · Jack Parker-Holder · Stephen Roberts -
2021 Poster: State Entropy Maximization with Random Encoders for Efficient Exploration »
Younggyo Seo · Lili Chen · Jinwoo Shin · Honglak Lee · Pieter Abbeel · Kimin Lee -
2021 Spotlight: State Entropy Maximization with Random Encoders for Efficient Exploration »
Younggyo Seo · Lili Chen · Jinwoo Shin · Honglak Lee · Pieter Abbeel · Kimin Lee -
2020 Poster: Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions »
Omer Gottesman · Joseph Futoma · Yao Liu · Sonali Parbhoo · Leo Celi · Emma Brunskill · Finale Doshi-Velez -
2020 Poster: Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling »
Yao Liu · Pierre-Luc Bacon · Emma Brunskill -
2020 Poster: Ready Policy One: World Building Through Active Learning »
Philip Ball · Jack Parker-Holder · Aldo Pacchiano · Krzysztof Choromanski · Stephen Roberts -
2019 : posters »
Zhengxing Chen · Juan Jose Garau Luis · Ignacio Albert Smet · Aditya Modi · Sabina Tomkins · Riley Simmons-Edler · Hongzi Mao · Alexander Irpan · Hao Lu · Rose Wang · Subhojyoti Mukherjee · Aniruddh Raghu · Syed Arbab Mohd Shihab · Byung Hoon Ahn · Rasool Fakoor · Pratik Chaudhari · Elena Smirnova · Min-hwan Oh · Xiaocheng Tang · Tony Qin · Qingyang Li · Marc Brittain · Ian Fox · Supratik Paul · Xiaofeng Gao · Yinlam Chow · Gabriel Dulac-Arnold · Ofir Nachum · Nikos Karampatziakis · Bharathan Balaji · Supratik Paul · Ali Davody · Djallel Bouneffouf · Himanshu Sahni · Soo Kim · Andrey Kolobov · Alexander Amini · Yao Liu · Xinshi Chen · · Craig Boutilier -
2019 : Spotlight »
Tyler Scott · Kiran Koshy · Jonathan Aigrain · Rene Bidart · Priyadarshini Panda · Dian Ang Yap · Yaniv Yacoby · Raphael Gontijo Lopes · Alberto Marchisio · Erik Englesson · Wanqian Yang · Moritz Graule · Yi Sun · Daniel Kang · Mike Dusenberry · Min Du · Hartmut Maennel · Kunal Menda · Vineet Edupuganti · Luke Metz · David Stutz · Vignesh Srinivasan · Timo Sämann · Vineeth N Balasubramanian · Sina Mohseni · Rob Cornish · Judith Butepage · Zhangyang Wang · Bai Li · Bo Han · Honglin Li · Maksym Andriushchenko · Lukas Ruff · Meet P. Vadera · Yaniv Ovadia · Sunil Thulasidasan · Disi Ji · Gang Niu · Saeed Mahloujifar · Aviral Kumar · SANGHYUK CHUN · Dong Yin · Joyce Xu Xu · Hugo Gomes · Raanan Rohekar -
2019 Poster: Combining parametric and nonparametric models for off-policy evaluation »
Omer Gottesman · Yao Liu · Scott Sussex · Emma Brunskill · Finale Doshi-Velez -
2019 Oral: Combining parametric and nonparametric models for off-policy evaluation »
Omer Gottesman · Yao Liu · Scott Sussex · Emma Brunskill · Finale Doshi-Velez -
2019 Poster: Diagnosing Bottlenecks in Deep Q-learning Algorithms »
Justin Fu · Aviral Kumar · Matthew Soh · Sergey Levine -
2019 Oral: Diagnosing Bottlenecks in Deep Q-learning Algorithms »
Justin Fu · Aviral Kumar · Matthew Soh · Sergey Levine