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Clement Gehring
Clement Gehring
Verified email at csail.mit.edu - Homepage
Title
Cited by
Cited by
Year
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
1282018
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
792013
Incremental truncated LSTD
C Gehring, Y Pan, M White
International Joint Conference on Artificial Intelligence, 2016
152016
Approximate Linear Successor Representation
CA Gehring
Multidisciplinary Conference on Reinforcement Learning and Decision Making …, 2015
112015
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
92021
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
42013
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
32019
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
32019
Sparse Coding Applied to Digit Recognition
C Gehring, S Lemay
sibi 1, 1, 2012
32012
Robust Reinforcement Learning: A Constrained Game-theoretic Approach
J Yu, C Gehring, F Schäfer, A Anandkumar
Learning for Dynamics and Control, 1242-1254, 2021
22021
Adaptable replanning with compressed linear action models for learning from demonstrations
C Gehring, LP Kaelbling, T Lozano-Perez
Conference on Robot Learning (CoRL), 2018
22018
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
12021
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
Motion Planning using Naturally Annoying Grammars (NAGs)
CR Garrett, C Gehring, G Goretkin, Z Mariet, Z Wang
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Articles 1–15