papers
Publications (14)
cs.LG2015
Towards Deep Neural Network Architectures Robust to Adversarial Examples
Shixiang Gu, Luca Rigazio
cs.LG2017
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning
Benjamin Eysenbach, Shixiang Gu, Julian Ibarz +1
cs.LG2018
Data-Efficient Hierarchical Reinforcement Learning
Ofir Nachum, Shixiang Gu, Honglak Lee +1
cs.LG2017
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control
Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau +3
stat.ML2017
Categorical Reparameterization with Gumbel-Softmax
Eric Jang, Shixiang Gu, Ben Poole
cs.AI2019
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
Ofir Nachum, Shixiang Gu, Honglak Lee +1
cs.LG2018
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
George Tucker, Dieterich Lawson, Shixiang Gu +1
cs.LG2019
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen +5
cs.RO2016
Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap +1
cs.LG2016
MuProp: Unbiased Backpropagation for Stochastic Neural Networks
Shixiang Gu, Sergey Levine, Ilya Sutskever +1
cs.LG2017
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani +2
cs.LG2018
The Mirage of Action-Dependent Baselines in Reinforcement Learning
George Tucker, Surya Bhupatiraju, Shixiang Gu +3
cs.LG2017
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning
Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani +3
cs.LG2015
Neural Adaptive Sequential Monte Carlo
Shixiang Gu, Zoubin Ghahramani, Richard E. Turner