| Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction W Sun, A Venkatraman, GJ Gordon, B Boots, JA Bagnell International Conference on Machine Learning, 2017 | 195 | 2017 |
| Model-based rl in contextual decision processes: Pac bounds and exponential improvements over model-free approaches W Sun, N Jiang, A Krishnamurthy, A Agarwal, J Langford Conference of Learning Theory (COLT 2019), 2019 | 119* | 2019 |
| Flambe: Structural complexity and representation learning of low rank mdps A Agarwal, S Kakade, A Krishnamurthy, W Sun Advances in neural information processing systems 33, 20095-20107, 2020 | 86 | 2020 |
| Imitation learning as f-divergence minimization L Ke, S Choudhury, M Barnes, W Sun, G Lee, S Srinivasa International Workshop on the Algorithmic Foundations of Robotics, 313-329, 2020 | 78 | 2020 |
| Reinforcement learning: Theory and algorithms A Agarwal, N Jiang, SM Kakade, W Sun CS Dept., UW Seattle, Seattle, WA, USA, Tech. Rep, 10-4, 2019 | 74* | 2019 |
| High-Frequency Replanning Under Uncertainty Using Parallel Sampling-Based Motion Planning W Sun, S Patil, R Alterovitz IEEE Transactions on Robotics 31 (1), 104 - 116, 2015 | 74 | 2015 |
| Corruption-robust exploration in episodic reinforcement learning T Lykouris, M Simchowitz, A Slivkins, W Sun Conference on Learning Theory, 3242-3245, 2021 | 70 | 2021 |
| Stochastic Extended LQR for Optimization-Based Motion Planning Under Uncertainty W Sun, J van den Berg, R Alterovitz IEEE Transactions on Automation Science and Engineering 13 (2), 437-447, 2016 | 70 | 2016 |
| Truncated Horizon Policy Search: Combining Reinforcement Learning and Imitation Learning W Sun, JA Bagnell, B Boots International Conference on Learning Representations (ICLR 2018), 2018 | 68 | 2018 |
| Bilinear classes: A structural framework for provable generalization in rl S Du, S Kakade, J Lee, S Lovett, G Mahajan, W Sun, R Wang International Conference on Machine Learning, 2826-2836, 2021 | 64 | 2021 |
| Pc-pg: Policy cover directed exploration for provable policy gradient learning A Agarwal, M Henaff, S Kakade, W Sun Advances in neural information processing systems 33, 13399-13412, 2020 | 64 | 2020 |
| Policy poisoning in batch reinforcement learning and control Y Ma, X Zhang, W Sun, J Zhu Advances in Neural Information Processing Systems 32, 2019 | 63 | 2019 |
| Information theoretic regret bounds for online nonlinear control S Kakade, A Krishnamurthy, K Lowrey, M Ohnishi, W Sun Advances in Neural Information Processing Systems 33, 15312-15325, 2020 | 59 | 2020 |
| Disagreement-Regularized Imitation Learning K Brantley, W Sun, M Henaff International Conference on Learning Representations (ICLR 2020), 2020 | 58 | 2020 |
| Provably Efficient Imitation Learning from Observation Alone W Sun, A Vemula, B Boots, JA Bagnell International Conference on Machine Learning, 2019 | 58 | 2019 |
| Dual Policy Iteration W Sun, G Gordon, JA Bagnell, B Boots Advances in Neural Information Processing Systems, 2018 | 56 | 2018 |
| Learning to filter with predictive state inference machines W Sun, A Venkatraman, B Boots, JA Bagnell International Conference on Machine Learning, 1197-1205, 2016 | 52 | 2016 |
| Safety-aware algorithms for adversarial contextual bandit W Sun, D Dey, A Kapoor International Conference on Machine Learning, 3280-3288, 2017 | 41 | 2017 |
| Sketching for Kronecker Product Regression and P-splines H Diao, Z Song, W Sun, DP Woodruff International Conference on Artificial Intelligence and Statistics, 2018 | 39 | 2018 |
| Predictive-state decoders: Encoding the future into recurrent networks A Venkatraman, N Rhinehart, W Sun, L Pinto, M Hebert, B Boots, K Kitani, ... Advances in Neural Information Processing Systems 30, 2017 | 38 | 2017 |
J. Andrew BagnellCarnegie Mellon UniversityVerified email at ri.cmu.edu
