| Improved techniques for training gans T Salimans, I Goodfellow, W Zaremba, V Cheung, A Radford, X Chen Advances in Neural Information Processing Systems, 2226-2234, 2016 | 9187 | 2016 |
| 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 | 4883 | 2016 |
| Improved variational inference with inverse autoregressive flow DP Kingma, T Salimans, R Jozefowicz, X Chen, I Sutskever, M Welling Advances in Neural Information Processing Systems, 4743-4751, 2016 | 1867 | 2016 |
| 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 | 1863 | 2016 |
| Evolution strategies as a scalable alternative to reinforcement learning T Salimans, J Ho, X Chen, S Sidor, I Sutskever arXiv preprint arXiv:1703.03864, 2017 | 1550 | 2017 |
| A Simple Neural Attentive Meta-Learner N Mishra, M Rohaninejad, X Chen, P Abbeel NIPS 2017 Workshop on Meta-Learning, 2017 | 1512* | 2017 |
| RL : 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 | 1010 | 2016 |
| Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications T Salimans, A Karpathy, X Chen, DP Kingma arXiv preprint arXiv:1701.05517, 2017 | 989 | 2017 |
| VIME: Variational information maximizing exploration R Houthooft, X Chen, Y Duan, J Schulman, F De Turck, P Abbeel Advances in Neural Information Processing Systems, 1109-1117, 2016 | 866 | 2016 |
| Variational Lossy Autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... International Conference on Learning Representations (ICLR), 2017 | 721 | 2017 |
| Parameter Space Noise for Exploration M Plappert, R Houthooft, P Dhariwal, S Sidor, RY Chen, X Chen, T Asfour, ... arXiv preprint arXiv:1706.01905, 2017 | 670 | 2017 |
| Evaluating Protein Transfer Learning with TAPE R Rao, N Bhattacharya, N Thomas, Y Duan, X Chen, J Canny, P Abbeel, ... arXiv preprint arXiv:1906.08230, 2019 | 638 | 2019 |
| Deep imitation learning for complex manipulation tasks from virtual reality teleoperation T Zhang, Z McCarthy, O Jowl, D Lee, X Chen, K Goldberg, P Abbeel 2018 IEEE International Conference on Robotics and Automation (ICRA), 1-8, 2018 | 626 | 2018 |
| # 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 | 613 | 2016 |
| Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design J Ho, X Chen, A Srinivas, Y Duan, P Abbeel arXiv preprint arXiv:1902.00275, 2019 | 406 | 2019 |
| Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules D Ho, E Liang, I Stoica, P Abbeel, X Chen arXiv preprint arXiv:1905.05393, 2019 | 401 | 2019 |
| Meta Learning Shared Hierarchies K Frans, J Ho, X Chen, P Abbeel, J Schulman arXiv preprint arXiv:1710.09767, 2017 | 389 | 2017 |
| Equivalence between policy gradients and soft q-learning J Schulman, X Chen, P Abbeel arXiv preprint arXiv:1704.06440, 2017 | 289 | 2017 |
| PixelSNAIL: An Improved Autoregressive Generative Model X Chen, N Mishra, M Rohaninejad, P Abbeel arXiv preprint arXiv:1712.09763, 2017 | 229 | 2017 |
| The Importance of Sampling inMeta-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 | 153* | 2018 |
Pieter AbbeelUC Berkeley | Covariant.AIVerified email at cs.berkeley.edu
