Philip Thomas

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Georgios TheocharousAdobe ResearchVerified email at adobe.com
Emma BrunskillAssociate Professor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Yash ChandakPostdoctoral Researcher, Stanford UniversityVerified email at stanford.edu
Bruno Castro da SilvaUniversity of MassachusettsVerified email at cs.umass.edu
Scott M. JordanPostdoctoral Fellow, University of AlbertaVerified email at ualberta.ca
Antonie J. (Ton) van den BogertProfessor of Mechanical Engineering, Cleveland State UniversityVerified email at csuohio.edu
Stephen GiguereUniversity of MassachusettsVerified email at cs.umass.edu
Scott NiekumAssociate Professor, University of Massachusetts AmherstVerified email at cs.umass.edu
George KonidarisBrownVerified email at cs.brown.edu
Chris NotaUniversity of Massachusetts, AmherstVerified email at cs.umass.edu
Yuriy BrunManning College of Information and Computer Sciences, University of Massachusetts AmherstVerified email at cs.umass.edu
Michael BranickyProfessor of Electrical Engineering & Computer Science, University of KansasVerified email at ku.edu
Sridhar MahadevanDirector, Data Science Lab, Adobe Research & Professor, University of Massachusetts, AmherstVerified email at cs.umass.edu
Erik Learned-MillerProfessor of Computer Science, University of Massachusetts AmherstVerified email at cs.umass.edu
Sarah OsentoskiIron OxVerified email at ironox.com
Will DabneyDeepMindVerified email at google.com
Blossom MetevierUniversity of Massachusetts AmherstVerified email at umass.edu
Robert KirschProfessor and Chair of Biomedical Engineering, Case Western Reserve UniversityVerified email at case.edu
Francisco M. GarciaUniversity of Massachusetts - AmherstVerified email at cs.umass.edu
Georg OstrovskiGoogle DeepMindVerified email at google.com

Philip Thomas

University of Massachusetts Amherst

Verified email at cs.umass.edu - Homepage

Artificial Intelligence Reinforcement Learning AI Safety


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Data-efficient off-policy policy evaluation for reinforcement learning P Thomas, E Brunskill International Conference on Machine Learning, 2139-2148, 2016	564	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	462	2011
High-confidence off-policy evaluation P Thomas, G Theocharous, M Ghavamzadeh Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015	266	2015
High confidence policy improvement P Thomas, G Theocharous, M Ghavamzadeh International Conference on Machine Learning, 2380-2388, 2015	194	2015
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	166	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	148	2016
Bias in natural actor-critic algorithms P Thomas International conference on machine learning, 441-448, 2014	145	2014
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	143	2019
Learning action representations for reinforcement learning Y Chandak, G Theocharous, J Kostas, S Jordan, P Thomas International conference on machine learning, 941-950, 2019	143	2019
Safe reinforcement learning PS Thomas	103	2015
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	56	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	56	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	55	2014
Is the policy gradient a gradient? C Nota, PS Thomas arXiv preprint arXiv:1906.07073, 2019	51	2019
Policy gradient methods for reinforcement learning with function approximation and action-dependent baselines PS Thomas, E Brunskill arXiv preprint arXiv:1706.06643, 2017	49	2017
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	47	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	46	2020
Use of atrial and bifocal cardiac pacemakers for treating resistant dysrhythmias. LS Dreifus, BV Berkovits, D Kimibiris, K Moghadam, G Haupt, P Walinsky, ... European Journal of Cardiology 3 (4), 257-266, 1975	45	1975
Using options and covariance testing for long horizon off-policy policy evaluation Z Guo, PS Thomas, E Brunskill Advances in Neural Information Processing Systems 30, 2017	43	2017
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	41	2019

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