Shixiang Gu

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 51

Top Topics

Temporal Difference

Gradient Estimators

Reinforcement Learning

Top Venues

Publications

Jian Zhang, Bin Pan, Wenxuan Shi, Yu Zhang, Shixiang Gu, Jinming Chen, Quanbin Xia
Construction of a High-Resolution Waterlogging Disaster Monitoring Framework Based on the APSIM Model: A Case Study of Jingzhou and Bengbu. Remote. Sens. 16 (14) (2024)
Jiaxin Huang, Shixiang Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, Jiawei Han
Large Language Models Can Self-Improve. EMNLP (2023)
Scott Fujimoto, Wei-Di Chang, Edward J. Smith, Shixiang Gu, Doina Precup, David Meger
For SALE: State-Action Representation Learning for Deep Reinforcement Learning. NeurIPS (2023)
Seyed Kamyar Seyed Ghasemipour, Daniel Freeman, Byron David, Shixiang Gu, Satoshi Kataoka, Igor Mordatch
Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning. CoRR (2022)
Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, Shixiang Gu
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization. ICLR (2021)
Archit Sharma, Michael Ahn, Sergey Levine, Vikash Kumar, Karol Hausman, Shixiang Gu
Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning. Robotics: Science and Systems (2020)
Archit Sharma, Michael Ahn, Sergey Levine, Vikash Kumar, Karol Hausman, Shixiang Gu
Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning. CoRR (2020)
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, Karol Hausman
Dynamics-Aware Unsupervised Discovery of Skills. ICLR (2020)
Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, Shixiang Gu
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization. CoRR (2020)
Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun, Craig Ferguson, Àgata Lapedriza, Noah Jones, Shixiang Gu, Rosalind W. Picard
Human-centric dialog training via offline reinforcement learning. EMNLP (1) (2020)
Lisa Lee, Benjamin Eysenbach, Ruslan Salakhutdinov, Shixiang Gu, Chelsea Finn
Weakly-Supervised Reinforcement Learning for Controllable Behavior. CoRR (2020)
Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Àgata Lapedriza, Noah Jones, Shixiang Gu, Rosalind W. Picard
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog. CoRR (2019)
Ofir Nachum, Michael Ahn, Hugo Ponte, Shixiang Gu, Vikash Kumar
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real. CoRR (2019)
Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning. ICLR (Poster) (2019)
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, Karol Hausman
Dynamics-Aware Unsupervised Discovery of Skills. CoRR (2019)
Yiding Jiang, Shixiang Gu, Kevin Murphy, Chelsea Finn
Language as an Abstraction for Hierarchical Deep Reinforcement Learning. NeurIPS (2019)
Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel, Shixiang Gu
A Divergence Minimization Perspective on Imitation Learning Methods. CoRR (2019)
George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. ICLR (Poster) (2019)
Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel, Shixiang Gu
A Divergence Minimization Perspective on Imitation Learning Methods. CoRL (2019)
Yiding Jiang, Shixiang Gu, Kevin Murphy, Chelsea Finn
Language as an Abstraction for Hierarchical Deep Reinforcement Learning. CoRR (2019)
Seyed Kamyar Seyed Ghasemipour, Shixiang Gu, Richard S. Zemel
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies. NeurIPS (2019)
Ofir Nachum, Haoran Tang, Xingyu Lu, Shixiang Gu, Honglak Lee, Sergey Levine
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning? CoRR (2019)
Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning. CoRR (2018)
George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. CoRR (2018)
Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning. ICLR (Poster) (2018)
George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine
The Mirage of Action-Dependent Baselines in Reinforcement Learning. CoRR (2018)
Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine
Data-Efficient Hierarchical Reinforcement Learning. CoRR (2018)
George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine
The Mirage of Action-Dependent Baselines in Reinforcement Learning. ICLR (Workshop) (2018)
George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine
The Mirage of Action-Dependent Baselines in Reinforcement Learning. ICML (2018)
Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine
Data-Efficient Hierarchical Reinforcement Learning. NeurIPS (2018)
Vitchyr Pong, Shixiang Gu, Murtaza Dalal, Sergey Levine
Temporal Difference Models: Model-Free Deep RL for Model-Based Control. CoRR (2018)
Vitchyr Pong, Shixiang Gu, Murtaza Dalal, Sergey Levine
Temporal Difference Models: Model-Free Deep RL for Model-Based Control. ICLR (Poster) (2018)
Eric Jang, Shixiang Gu, Ben Poole
Categorical Reparameterization with Gumbel-Softmax. ICLR (Poster) (2017)
Shixiang Gu, Ethan Holly, Timothy P. Lillicrap, Sergey Levine
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. ICRA (2017)
Natasha Jaques, Shixiang Gu, Richard E. Turner, Douglas Eck
Tuning Recurrent Neural Networks with Reinforcement Learning. ICLR (Workshop) (2017)
Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Bernhard Schölkopf, Sergey Levine
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. CoRR (2017)
Shixiang Gu, Tim Lillicrap, Richard E. Turner, Zoubin Ghahramani, Bernhard Schölkopf, Sergey Levine
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. NIPS (2017)
Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control. ICML (2017)
Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. ICLR (2017)
Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning. CoRR (2017)
Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine
Continuous Deep Q-Learning with Model-based Acceleration. CoRR (2016)
Shixiang Gu, Ethan Holly, Timothy P. Lillicrap, Sergey Levine
Deep Reinforcement Learning for Robotic Manipulation. CoRR (2016)
Eric Jang, Shixiang Gu, Ben Poole
Categorical Reparameterization with Gumbel-Softmax. CoRR (2016)
Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. CoRR (2016)
Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih
MuProp: Unbiased Backpropagation for Stochastic Neural Networks. ICLR (Poster) (2016)
Natasha Jaques, Shixiang Gu, Richard E. Turner, Douglas Eck
Tuning Recurrent Neural Networks with Reinforcement Learning. CoRR (2016)
Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine
Continuous Deep Q-Learning with Model-based Acceleration. ICML (2016)
Shixiang Gu, Luca Rigazio
Towards Deep Neural Network Architectures Robust to Adversarial Examples. ICLR (Workshop) (2015)
Shixiang Gu, Zoubin Ghahramani, Richard E. Turner
Neural Adaptive Sequential Monte Carlo. CoRR (2015)
Nilesh Tripuraneni, Shixiang Gu, Hong Ge, Zoubin Ghahramani
Particle Gibbs for Infinite Hidden Markov Models. NIPS (2015)
Shixiang Gu, Zoubin Ghahramani, Richard E. Turner
Neural Adaptive Sequential Monte Carlo. NIPS (2015)
Steve Mann, Raymond Chun Hing Lo, Kalin Ovtcharov, Shixiang Gu, David Dai, Calvin Ngan, Tao Ai
Realtime HDR (High Dynamic Range) video for eyetap wearable computers, FPGA-based seeing aids, and glasseyes (EyeTaps). CCECE (2012)