| Batched large-scale bayesian optimization in high-dimensional spaces Z Wang, C Gehring, P Kohli, S Jegelka International Conference on Artificial Intelligence and Statistics, 745-754, 2018 | 209 | 2018 |
| Smart exploration in reinforcement learning using absolute temporal difference errors C Gehring, D Precup Proceedings of the 2013 international conference on Autonomous agents and …, 2013 | 111 | 2013 |
| Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators C Gehring, M Asai, R Chitnis, T Silver, LP Kaelbling, S Sohrabi, M Katz arXiv, 2021 | 36 | 2021 |
| Robust Reinforcement Learning: A Constrained Game-theoretic Approach J Yu, C Gehring, F Schäfer, A Anandkumar Learning for Dynamics and Control, 1242-1254, 2021 | 16 | 2021 |
| Incremental truncated LSTD C Gehring, Y Pan, M White International Joint Conference on Artificial Intelligence, 2016 | 15 | 2016 |
| Approximate Linear Successor Representation CA Gehring Multidisciplinary Conference on Reinforcement Learning and Decision Making …, 2015 | 12 | 2015 |
| Bridging State and History Representations: Understanding Self-Predictive RL T Ni, B Eysenbach, E Seyedsalehi, M Ma, C Gehring, A Mahajan, ... arXiv preprint arXiv:2401.08898, 2024 | 6 | 2024 |
| A Lagrangian Method for Inverse Problems in Reinforcement Learning PL Bacon, F Schäfer, C Gehring, A Anandkumar, E Brunskill NeurIPS Optimization Foundations for Reinforcement Learning Workshop, 2019 | 6 | 2019 |
| Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization C Gehring, K Kawaguchi, J Huang, L Kaelbling Advances in Neural Information Processing Systems 34, 703-714, 2021 | 5 | 2021 |
| Comment on “Giant electromechanical coupling of relaxor ferroelectrics controlled by polar nanoregion vibrations” PM Gehring, Z Xu, C Stock, G Xu, D Parshall, L Harriger, CA Gehring, X Li, ... Science advances 5 (3), eaar5066, 2019 | 4 | 2019 |
| Reinforcement Learning Competition: Helicopter Hovering with Controllability and Kernel-Based Stochastic Factorization A Asbah, AMS Barreto, C Gehring, J Pineau, D Precup Proceedings of International Conference on Machine Learning (ICML …, 2013 | 4 | 2013 |
| Adaptable replanning with compressed linear action models for learning from demonstrations C Gehring, LP Kaelbling, T Lozano-Perez Conference on Robot Learning (CoRL), 2018 | 3 | 2018 |
| Sparse Coding Applied to Digit Recognition C Gehring, S Lemay sibi 1, 1, 2012 | 3 | 2012 |
| Course Correcting Koopman Representations M Fathi, C Gehring, J Pilault, D Kanaa, PL Bacon, R Goroshin arXiv preprint arXiv:2310.15386, 2023 | 1 | 2023 |
| Neural differential equations for temperature control in buildings under demand response programs V Taboga, C Gehring, M Le Cam, H Dagdougui, PL Bacon Applied Energy 368, 123433, 2024 | | 2024 |
| Do Transformer World Models Give Better Policy Gradients? M Ma, T Ni, C Gehring, P D'Oro, PL Bacon arXiv preprint arXiv:2402.05290, 2024 | | 2024 |
| A Differentiable Sequence Model Perspective on Policy Gradients M Ma, P D'Oro, T Ni, C Gehring, PL Bacon | | 2023 |
| Functional Risk Minimization F Alet, C Gehring, T Lozano-Pérez, JB Tenenbaum, LP Kaelbling | | 2022 |
| Efficient reinforcement learning via singular value decomposition, end-to-end model-based methods and reward shaping C Gehring Massachusetts Institute of Technology, 2022 | | 2022 |
| Shape Fitting Temporal Difference Learning C Gehring McGill University (Canada), 2015 | | 2015 |
Leslie KaelblingVerified email at csail.mit.edu
