| Touchdown: Natural language navigation and spatial reasoning in visual street environments H Chen, A Suhr, D Misra, N Snavely, Y Artzi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 393 | 2019 |
| Tell me Dave: Context-sensitive grounding of natural language to manipulation instructions DK Misra, J Sung, K Lee, A Saxena The International Journal of Robotics Research 35 (1-3), 281-300, 2016 | 360 | 2016 |
| Mapping instructions and visual observations to actions with reinforcement learning D Misra, J Langford, Y Artzi arXiv preprint arXiv:1704.08795, 2017 | 277 | 2017 |
| Robobrain: Large-scale knowledge engine for robots A Saxena, A Jain, O Sener, A Jami, DK Misra, HS Koppula arXiv preprint arXiv:1412.0691, 2014 | 229 | 2014 |
| Kinematic state abstraction and provably efficient rich-observation reinforcement learning D Misra, M Henaff, A Krishnamurthy, J Langford International conference on machine learning, 6961-6971, 2020 | 196 | 2020 |
| Mapping instructions to actions in 3d environments with visual goal prediction D Misra, A Bennett, V Blukis, E Niklasson, M Shatkhin, Y Artzi arXiv preprint arXiv:1809.00786, 2018 | 195 | 2018 |
| Lipschitz continuity in model-based reinforcement learning K Asadi, D Misra, M Littman International Conference on Machine Learning, 264-273, 2018 | 185 | 2018 |
| Understanding contrastive learning requires incorporating inductive biases N Saunshi, J Ash, S Goel, D Misra, C Zhang, S Arora, S Kakade, ... International Conference on Machine Learning, 19250-19286, 2022 | 121 | 2022 |
| Chalet: Cornell house agent learning environment C Yan, D Misra, A Bennnett, A Walsman, Y Bisk, Y Artzi arXiv preprint arXiv:1801.07357, 2018 | 110 | 2018 |
| Mapping navigation instructions to continuous control actions with position-visitation prediction V Blukis, D Misra, RA Knepper, Y Artzi Conference on Robot Learning, 505-518, 2018 | 91 | 2018 |
| Environment-driven lexicon induction for high-level instructions D Misra, K Tao, P Liang, A Saxena Proceedings of the 53rd Annual Meeting of the Association for Computational …, 2015 | 82 | 2015 |
| Neural shift-reduce ccg semantic parsing DK Misra, Y Artzi Proceedings of the Conference on Empirical Methods in Natural Language …, 2016 | 61 | 2016 |
| Combating the compounding-error problem with a multi-step model K Asadi, D Misra, S Kim, ML Littman arXiv preprint arXiv:1905.13320, 2019 | 60 | 2019 |
| Investigating the role of negatives in contrastive representation learning JT Ash, S Goel, A Krishnamurthy, D Misra arXiv preprint arXiv:2106.09943, 2021 | 54 | 2021 |
| Provably filtering exogenous distractors using multistep inverse dynamics Y Efroni, D Misra, A Krishnamurthy, A Agarwal, J Langford International Conference on Learning Representations, 2021 | 52* | 2021 |
| Learning the linear quadratic regulator from nonlinear observations Z Mhammedi, DJ Foster, M Simchowitz, D Misra, W Sun, A Krishnamurthy, ... Advances in Neural Information Processing Systems 33, 14532-14543, 2020 | 50 | 2020 |
| Interactive learning from activity description KX Nguyen, D Misra, R Schapire, M Dudík, P Shafto International Conference on Machine Learning, 8096-8108, 2021 | 42 | 2021 |
| The Truth Is In There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction P Sharma, JT Ash, D Misra The Twelfth International Conference on Learning Representations, 2023 | 33 | 2023 |
| Learning to generate better than your llm JD Chang, K Brantley, R Ramamurthy, D Misra, W Sun arXiv preprint arXiv:2306.11816, 2023 | 32 | 2023 |
| Policy shaping and generalized update equations for semantic parsing from denotations D Misra, MW Chang, X He, W Yih arXiv preprint arXiv:1809.01299, 2018 | 27 | 2018 |
John LangfordMicrosoft Research New YorkVerified email at hunch.net
