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Yan Duan
Yan Duan
Covariant.AI
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Title
Cited by
Cited by
Year
InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
X Chen, Y Duan, R Houthooft, J Schulman, I Sutskever, P Abbeel
Advances in Neural Information Processing Systems, 2172-2180, 2016
36202016
Benchmarking deep reinforcement learning for continuous control
Y Duan, X Chen, R Houthooft, J Schulman, P Abbeel
International conference on machine learning, 1329-1338, 2016
14492016
RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning
Y Duan, J Schulman, X Chen, PL Bartlett, I Sutskever, P Abbeel
arXiv preprint arXiv:1611.02779, 2016
6892016
Vime: Variational information maximizing exploration
R Houthooft, X Chen, Y Duan, J Schulman, F De Turck, P Abbeel
Advances in neural information processing systems 29, 2016
6582016
Variational lossy autoencoder
X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ...
arXiv preprint arXiv:1611.02731, 2016
5732016
Motion planning with sequential convex optimization and convex collision checking
J Schulman, Y Duan, J Ho, A Lee, I Awwal, H Bradlow, J Pan, S Patil, ...
The International Journal of Robotics Research 33 (9), 1251-1270, 2014
5602014
Adversarial attacks on neural network policies
S Huang, N Papernot, I Goodfellow, Y Duan, P Abbeel
arXiv preprint arXiv:1702.02284, 2017
5562017
One-shot imitation learning
Y Duan, M Andrychowicz, B Stadie, OAI Jonathan Ho, J Schneider, ...
Advances in neural information processing systems 30, 2017
5522017
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
H Tang, R Houthooft, D Foote, A Stooke, X Chen, Y Duan, J Schulman, ...
arXiv preprint arXiv:1611.04717, 2016
5362016
Deep Spatial Autoencoders for Visuomotor Learning
C Finn, XY Tan, Y Duan, T Darrell, S Levine, P Abbeel
International Conference on Robotics and Automation (ICRA), 2016
522*2016
Model-ensemble trust-region policy optimization
T Kurutach, I Clavera, Y Duan, A Tamar, P Abbeel
arXiv preprint arXiv:1802.10592, 2018
3152018
Stochastic neural networks for hierarchical reinforcement learning
C Florensa, Y Duan, P Abbeel
arXiv preprint arXiv:1704.03012, 2017
3072017
Evaluating protein transfer learning with TAPE
R Rao, N Bhattacharya, N Thomas, Y Duan, P Chen, J Canny, P Abbeel, ...
Advances in neural information processing systems 32, 2019
2842019
Flow++: Improving flow-based generative models with variational dequantization and architecture design
J Ho, X Chen, A Srinivas, Y Duan, P Abbeel
International Conference on Machine Learning, 2722-2730, 2019
2192019
Variance reduction for policy gradient with action-dependent factorized baselines
C Wu, A Rajeswaran, Y Duan, V Kumar, AM Bayen, S Kakade, I Mordatch, ...
arXiv preprint arXiv:1803.07246, 2018
1162018
The Importance of Sampling in Meta-Reinforcement Learning
B Stadie, G Yang, R Houthooft, P Chen, Y Duan, Y Wu, P Abbeel, ...
Advances in Neural Information Processing Systems, 9299-9309, 2018
101*2018
Deep unsupervised cardinality estimation
Z Yang, E Liang, A Kamsetty, C Wu, Y Duan, X Chen, P Abbeel, ...
arXiv preprint arXiv:1905.04278, 2019
952019
Attacking machine learning with adversarial examples
I Goodfellow, N Papernot, S Huang, Y Duan, P Abbeel, J Clark
OpenAI Blog 24, 2017
552017
Advances in neural information processing systems
J Chang, S Gerrish, C Wang, JL Boyd-Graber, DM Blei, Y Bengio, ...
Reading Tea Leaves: How Humans Interpret Topic Models, 296-299, 2009
51*2009
NeuroCard: one cardinality estimator for all tables
Z Yang, A Kamsetty, S Luan, E Liang, Y Duan, X Chen, I Stoica
arXiv preprint arXiv:2006.08109, 2020
422020
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