| Open X-Embodiment: Robotic learning datasets and RT-X models OXE Collaboration, A O’Neill, A Rehman, A Gupta, A Maddukuri, A Gupta, ... arXiv preprint arXiv:2310.08864 1 (2), 2023 | 919* | 2023 |
| BLOG: Probabilistic Models with Unknown Objects B Milch, B Marthi, S Russell, D Sontag, DL Ong, A Kolobov Statistical relational learning, 373, 2007 | 640 | 2007 |
| Planning with Markov decision processes: An AI perspective Mausam, A Kolobov Synthesis Lectures on Artificial Intelligence and Machine Learning 6 (1), 1-210, 2012 | 244* | 2012 |
| Parallel task routing for crowdsourcing J Bragg, A Kolobov, M Mausam, D Weld Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 2 …, 2014 | 215* | 2014 |
| Introduction to statistical relational learning D Koller, N Friedman, S Džeroski, C Sutton, A McCallum, A Pfeffer, ... MIT press, 2007 | 190 | 2007 |
| Interactive teaching strategies for agent training O Amir, E Kamar, A Kolobov, B Grosz IJCAI 2016, 2016 | 160 | 2016 |
| Tracevla: Visual trace prompting enhances spatial-temporal awareness for generalist robotic policies R Zheng, Y Liang, S Huang, J Gao, H Daumé III, A Kolobov, F Huang, ... arXiv preprint arXiv:2412.10345, 2024 | 126 | 2024 |
| Safe reinforcement learning via curriculum induction M Turchetta, A Kolobov, S Shah, A Krause, A Agarwal Advances in Neural Information Processing Systems 33, 12151-12162, 2020 | 123 | 2020 |
| Heuristic search for generalized stochastic shortest path MDPs A Kolobov, Mausam, DS Weld, H Geffner Twenty-First International Conference on Automated Planning and Scheduling, 2011 | 115* | 2011 |
| A Theory of Goal-Oriented MDPs with Dead Ends A Kolobov, Mausam, DS Weld UAI, 2012 | 114* | 2012 |
| Heuristic-guided reinforcement learning CA Cheng, A Kolobov, A Swaminathan Advances in Neural Information Processing Systems 34, 13550-13563, 2021 | 111 | 2021 |
| LRTDP vs. UCT for Online Probabilistic Planning A Kolobov, Mausam, DS Weld Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012 | 80* | 2012 |
| Approximate inference for infinite contingent Bayesian networks B Milch, B Marthi, D Sontag, S Russell, DL Ong, A Kolobov AISTATS, 2005 | 74 | 2005 |
| Reverse Iterative Deepening for Finite-Horizon MDPs with Large Branching Factors A Kolobov, P Dai, Mausam, DS Weld Proceedings of the 22nd International Conference on Automated Planning and …, 2012 | 55* | 2012 |
| Policy improvement via imitation of multiple oracles CA Cheng, A Kolobov, A Agarwal Advances in Neural Information Processing Systems 33, 5587-5598, 2020 | 52 | 2020 |
| Metareasoning for planning under uncertainty CH Lin, A Kolobov, E Kamar, E Horvitz arXiv preprint arXiv:1505.00399, 2015 | 46 | 2015 |
| Goal representations for instruction following: A semi-supervised language interface to control V Myers, AW He, K Fang, HR Walke, P Hansen-Estruch, CA Cheng, ... Conference on Robot Learning, 3894-3908, 2023 | 43 | 2023 |
| ReTrASE: Integrating paradigms for approximate probabilistic planning A Kolobov, Mausam, DS Weld Twenty-First International Joint Conference on Artificial Intelligence, 1746 …, 2009 | 42 | 2009 |
| Selecting robust strategies in RTS games via concurrent plan augmentation A Elogeel | 41 | 2015 |
| The importance of directional feedback for llm-based optimizers A Nie, CA Cheng, A Kolobov, A Swaminathan arXiv preprint arXiv:2405.16434, 2024 | 38 | 2024 |
