Philip Thomas

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Georgios TheocharousAdobe Research確認したメールアドレス: adobe.com
Emma BrunskillAssociate Professor of Computer Science, Stanford University確認したメールアドレス: cs.stanford.edu
Bruno Castro da SilvaUniversity of Massachusetts確認したメールアドレス: cs.umass.edu
Scott M. JordanPostdoctoral Fellow, University of Alberta確認したメールアドレス: ualberta.ca
Scott NiekumAssociate Professor, University of Massachusetts Amherst確認したメールアドレス: cs.umass.edu
Antonie J. (Ton) van den BogertProfessor of Mechanical Engineering, Cleveland State University確認したメールアドレス: csuohio.edu
George KonidarisBrown確認したメールアドレス: cs.brown.edu
Stephen GiguereUniversity of Massachusetts確認したメールアドレス: cs.umass.edu
Yuriy BrunManning College of Information and Computer Sciences, University of Massachusetts Amherst確認したメールアドレス: cs.umass.edu
Chris NotaUniversity of Massachusetts, Amherst確認したメールアドレス: cs.umass.edu
Michael BranickyProfessor of Electrical Engineering & Computer Science, University of Kansas確認したメールアドレス: ku.edu
Erik Learned-MillerProfessor of Computer Science, University of Massachusetts Amherst確認したメールアドレス: cs.umass.edu
Blossom MetevierUniversity of Massachusetts Amherst確認したメールアドレス: umass.edu
Sarah OsentoskiVinci4d確認したメールアドレス: vinci4d.ai
Sridhar MahadevanDirector, Adobe Research & Professor, University of Massachusetts, Amherst確認したメールアドレス: cs.umass.edu
Will DabneyDeepMind確認したメールアドレス: google.com
Francisco M. GarciaUniversity of Massachusetts - Amherst確認したメールアドレス: cs.umass.edu
Robert KirschProfessor and Chair of Biomedical Engineering, Case Western Reserve University確認したメールアドレス: case.edu
Peter StoneProfessor of Computer Science, The University of Texas at Austin確認したメールアドレス: cs.utexas.edu
Arthur GuezGoogle DeepMind確認したメールアドレス: google.com

フォロー

Philip Thomas

University of Massachusetts Amherst

確認したメールアドレス: cs.umass.edu - ホームページ

Artificial Intelligence Reinforcement Learning AI Safety


タイトル引用回数順公開年順タイトル順	引用先引用先	年
Data-efficient off-policy policy evaluation for reinforcement learning P Thomas, E Brunskill International conference on machine learning, 2139-2148, 2016	785	2016
Value function approximation in reinforcement learning using the Fourier basis G Konidaris, S Osentoski, P Thomas Proceedings of the AAAI conference on artificial intelligence 25 (1), 380-385, 2011	558	2011
High-confidence off-policy evaluation P Thomas, G Theocharous, M Ghavamzadeh Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015	346	2015
High confidence policy improvement P Thomas, G Theocharous, M Ghavamzadeh International Conference on Machine Learning, 2380-2388, 2015	229	2015
Learning action representations for reinforcement learning Y Chandak, G Theocharous, J Kostas, S Jordan, P Thomas International conference on machine learning, 941-950, 2019	220	2019
Preventing undesirable behavior of intelligent machines P Thomas, B Castro da Silva, A Barto, S Giguere, Y Brun, E Brunskill Science 366 (6468), 999-1004, 2019	210	2019
Ad recommendation systems for life-time value optimization G Theocharous, PS Thomas, M Ghavamzadeh Proceedings of the 24th international conference on world wide web, 1305-1310, 2015	209	2015
Increasing the action gap: New operators for reinforcement learning MG Bellemare, G Ostrovski, A Guez, P Thomas, R Munos Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016	182	2016
Bias in natural actor-critic algorithms P Thomas International conference on machine learning, 441-448, 2014	172	2014
Safe reinforcement learning PS Thomas University of Massachusetts Libraries, 2015	127	2015
Optimization and evaluation of a proportional derivative controller for planar arm movement KM Jagodnik, AJ van den Bogert Journal of Biomechanics 43 (6), 1086-1091, 2010	89	2010
Is the policy gradient a gradient? C Nota, PS Thomas arXiv preprint arXiv:1906.07073, 2019	86	2019
Optimizing for the future in non-stationary mdps Y Chandak, G Theocharous, S Shankar, M White, S Mahadevan, ... International Conference on Machine Learning, 1414-1425, 2020	79	2020
Evaluating the performance of reinforcement learning algorithms S Jordan, Y Chandak, D Cohen, M Zhang, P Thomas International Conference on Machine Learning, 4962-4973, 2020	78	2020
Training an actor-critic reinforcement learning controller for arm movement using human-generated rewards KM Jagodnik, PS Thomas, AJ van den Bogert, MS Branicky, RF Kirsch IEEE Transactions on Neural Systems and Rehabilitation Engineering 25 (10 …, 2017	74	2017
Proximal reinforcement learning: A new theory of sequential decision making in primal-dual spaces S Mahadevan, B Liu, P Thomas, W Dabney, S Giguere, N Jacek, I Gemp, ... arXiv preprint arXiv:1405.6757, 2014	74	2014
Policy gradient methods for reinforcement learning with function approximation and action-dependent baselines PS Thomas, E Brunskill arXiv preprint arXiv:1706.06643, 2017	72	2017
Predictive off-policy policy evaluation for nonstationary decision problems, with applications to digital marketing P Thomas, G Theocharous, M Ghavamzadeh, I Durugkar, E Brunskill Proceedings of the AAAI Conference on Artificial Intelligence 31 (2), 4740-4745, 2017	67	2017
Risk Quantification for Policy Deployment PS Thomas, G Theocharous, M Ghavamzadeh US Patent App. 14/552,047, 2016	65	2016
Offline contextual bandits with high probability fairness guarantees B Metevier, S Giguere, S Brockman, A Kobren, Y Brun, E Brunskill, ... Advances in neural information processing systems 32, 2019	63	2019

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