| One-shot learning for semantic segmentation A Shaban, S Bansal, Z Liu, I Essa, B Boots arXiv preprint arXiv:1709.03410, 2017 | 842 | 2017 |
| Information theoretic mpc for model-based reinforcement learning G Williams, N Wagener, B Goldfain, P Drews, JM Rehg, B Boots, ... 2017 IEEE international conference on robotics and automation (ICRA), 1714-1721, 2017 | 667 | 2017 |
| Differentiable mpc for end-to-end planning and control B Amos, I Jimenez, J Sacks, B Boots, JZ Kolter Advances in neural information processing systems 31, 2018 | 457 | 2018 |
| Agile autonomous driving using end-to-end deep imitation learning Y Pan, CA Cheng, K Saigol, K Lee, X Yan, E Theodorou, B Boots arXiv preprint arXiv:1709.07174, 2017 | 385 | 2017 |
| Truncated back-propagation for bilevel optimization A Shaban, CA Cheng, N Hatch, B Boots The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 298 | 2019 |
| Closing the learning-planning loop with predictive state representations B Boots, SM Siddiqi, GJ Gordon The International Journal of Robotics Research 30 (7), 954-966, 2011 | 288 | 2011 |
| Deeply aggrevated: Differentiable imitation learning for sequential prediction W Sun, A Venkatraman, GJ Gordon, B Boots, JA Bagnell International conference on machine learning, 3309-3318, 2017 | 286 | 2017 |
| Hilbert space embeddings of hidden Markov models L Song, B Boots, S Siddiqi, G Gordon, A Smola | 245 | 2010 |
| Continuous-time Gaussian process motion planning via probabilistic inference M Mukadam, J Dong, X Yan, F Dellaert, B Boots The International Journal of Robotics Research 37 (11), 1319-1340, 2018 | 240 | 2018 |
| Imitation learning for agile autonomous driving Y Pan, CA Cheng, K Saigol, K Lee, X Yan, EA Theodorou, B Boots The International Journal of Robotics Research 39 (2-3), 286-302, 2020 | 178 | 2020 |
| Gaussian process motion planning M Mukadam, X Yan, B Boots 2016 IEEE international conference on robotics and automation (ICRA), 9-15, 2016 | 169 | 2016 |
| Motion planning as probabilistic inference using Gaussian processes and factor graphs. J Dong, M Mukadam, F Dellaert, B Boots Robotics: Science and Systems 12 (4), 2016 | 165 | 2016 |
| A constraint generation approach to learning stable linear dynamical systems S Siddiqi, B Boots, G Gordon CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF, 2008 | 158* | 2008 |
| Learning from conditional distributions via dual embeddings B Dai, N He, Y Pan, B Boots, L Song Artificial Intelligence and Statistics, 1458-1467, 2017 | 153 | 2017 |
| Reduced-rank hidden Markov models S Siddiqi, B Boots, G Gordon Proceedings of the Thirteenth International Conference on Artificial …, 2010 | 146 | 2010 |
| 4D crop monitoring: Spatio-temporal reconstruction for agriculture J Dong, JG Burnham, B Boots, G Rains, F Dellaert 2017 IEEE International Conference on Robotics and Automation (ICRA), 3878-3885, 2017 | 135 | 2017 |
| Storm: An integrated framework for fast joint-space model-predictive control for reactive manipulation M Bhardwaj, B Sundaralingam, A Mousavian, ND Ratliff, D Fox, F Ramos, ... Conference on Robot Learning, 750-759, 2022 | 129 | 2022 |
| Iris: Implicit reinforcement without interaction at scale for learning control from offline robot manipulation data A Mandlekar, F Ramos, B Boots, S Savarese, L Fei-Fei, A Garg, D Fox 2020 IEEE International Conference on Robotics and Automation (ICRA), 4414-4420, 2020 | 129 | 2020 |
| Hilbert space embeddings of predictive state representations B Boots, G Gordon, A Gretton arXiv preprint arXiv:1309.6819, 2013 | 115 | 2013 |
| Provably efficient imitation learning from observation alone W Sun, A Vemula, B Boots, D Bagnell International conference on machine learning, 6036-6045, 2019 | 112 | 2019 |
Ching-An ChengMicrosoft ResearchVerified email at microsoft.com
