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Variable Skipping for Autoregressive Range Density Estimation

Jul 10, 2020
Eric Liang, Zongheng Yang, Ion Stoica, Pieter Abbeel, Yan Duan, Xi Chen

* ICML 2020. Code released at: https://var-skip.github.io/ 

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NeuroCard: One Cardinality Estimator for All Tables

Jun 15, 2020
Zongheng Yang, Amog Kamsetty, Sifei Luan, Eric Liang, Yan Duan, Xi Chen, Ion Stoica


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Evaluating Protein Transfer Learning with TAPE

Jun 19, 2019
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, Yun S. Song

* 20 pages, 4 figures 

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Selectivity Estimation with Deep Likelihood Models

May 10, 2019
Zongheng Yang, Eric Liang, Amog Kamsetty, Chenggang Wu, Yan Duan, Xi Chen, Pieter Abbeel, Joseph M. Hellerstein, Sanjay Krishnan, Ion Stoica


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Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

Feb 01, 2019
Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel

* 16 pages 

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Model-Ensemble Trust-Region Policy Optimization

Oct 05, 2018
Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel


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Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

Mar 20, 2018
Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M Bayen, Sham Kakade, Igor Mordatch, Pieter Abbeel

* Accepted to ICLR 2018, Oral (2%) 

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Some Considerations on Learning to Explore via Meta-Reinforcement Learning

Mar 03, 2018
Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever


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#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

Dec 05, 2017
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel

* 10 pages main text + 10 pages supplementary. Published at NIPS 2017 

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One-Shot Imitation Learning

Dec 04, 2017
Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba


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Stochastic Neural Networks for Hierarchical Reinforcement Learning

Apr 10, 2017
Carlos Florensa, Yan Duan, Pieter Abbeel

* International Conference on Learning Representations 2017 
* Published as a conference paper at ICLR 2017 

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Variational Lossy Autoencoder

Mar 04, 2017
Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel

* Added CIFAR10 experiments; ICLR 2017 

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Adversarial Attacks on Neural Network Policies

Feb 08, 2017
Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, Pieter Abbeel


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VIME: Variational Information Maximizing Exploration

Jan 27, 2017
Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel

* Published in Advances in Neural Information Processing Systems 29 (NIPS), pages 1109-1117 

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RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

Nov 10, 2016
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel

* 14 pages. Under review as a conference paper at ICLR 2017 

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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

Jun 12, 2016
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel


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Benchmarking Deep Reinforcement Learning for Continuous Control

May 27, 2016
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel

* 14 pages, ICML 2016 

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Deep Spatial Autoencoders for Visuomotor Learning

Mar 01, 2016
Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, Pieter Abbeel

* Published in the International Conference on Robotics and Automation (ICRA) 

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