Philip Thomas 个人学术档案

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	总计	2019 年至今
引用	4612	3595
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i10 指数	58	52

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Georgios TheocharousAdobe Research在 adobe.com 的电子邮件经过验证
Emma BrunskillAssociate Professor of Computer Science, Stanford University在 cs.stanford.edu 的电子邮件经过验证
Bruno Castro da SilvaUniversity of Massachusetts在 cs.umass.edu 的电子邮件经过验证
Scott M. JordanPostdoctoral Fellow, University of Alberta在 ualberta.ca 的电子邮件经过验证
George KonidarisBrown在 cs.brown.edu 的电子邮件经过验证
Scott NiekumAssociate Professor, University of Massachusetts Amherst在 cs.umass.edu 的电子邮件经过验证
Stephen GiguereUniversity of Massachusetts在 cs.umass.edu 的电子邮件经过验证
Yuriy BrunManning College of Information and Computer Sciences, University of Massachusetts Amherst在 cs.umass.edu 的电子邮件经过验证
Antonie J. (Ton) van den BogertProfessor of Mechanical Engineering, Cleveland State University在 csuohio.edu 的电子邮件经过验证
Chris NotaUniversity of Massachusetts, Amherst在 cs.umass.edu 的电子邮件经过验证
Michael BranickyProfessor of Electrical Engineering & Computer Science, University of Kansas在 ku.edu 的电子邮件经过验证
Erik Learned-MillerProfessor of Computer Science, University of Massachusetts Amherst在 cs.umass.edu 的电子邮件经过验证
Sarah OsentoskiVinci4d在 vinci4d.ai 的电子邮件经过验证
Blossom MetevierUniversity of Massachusetts Amherst在 umass.edu 的电子邮件经过验证
Sridhar MahadevanDirector, Data Science Lab, Adobe Research & Professor, University of Massachusetts, Amherst在 cs.umass.edu 的电子邮件经过验证
Will DabneyDeepMind在 google.com 的电子邮件经过验证
Francisco M. GarciaUniversity of Massachusetts - Amherst在 cs.umass.edu 的电子邮件经过验证
Robert KirschProfessor and Chair of Biomedical Engineering, Case Western Reserve University在 case.edu 的电子邮件经过验证
Arthur GuezGoogle DeepMind在 google.com 的电子邮件经过验证
Rémi MunosGoogle DeepMind在 inria.fr 的电子邮件经过验证

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Philip Thomas

University of Massachusetts Amherst

在 cs.umass.edu 的电子邮件经过验证 - 首页

Artificial Intelligence Reinforcement Learning AI Safety


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Data-efficient off-policy policy evaluation for reinforcement learning P Thomas, E Brunskill International Conference on Machine Learning, 2139-2148, 2016	722	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	545	2011
High-confidence off-policy evaluation P Thomas, G Theocharous, M Ghavamzadeh Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015	315	2015
High confidence policy improvement P Thomas, G Theocharous, M Ghavamzadeh International Conference on Machine Learning, 2380-2388, 2015	220	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	198	2015
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	195	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	187	2019
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	170	2016
Bias in natural actor-critic algorithms P Thomas International conference on machine learning, 441-448, 2014	158	2014
Safe reinforcement learning PS Thomas	119	2015
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	71	2020
Is the policy gradient a gradient? C Nota, PS Thomas arXiv preprint arXiv:1906.07073, 2019	70	2019
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	69	2014
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	67	2017
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	66	2020
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	64	2017
Policy gradient methods for reinforcement learning with function approximation and action-dependent baselines PS Thomas, E Brunskill arXiv preprint arXiv:1706.06643, 2017	62	2017
Importance Sampling for Fair Policy Selection. S Doroudi, PS Thomas, E Brunskill Grantee Submission, 2017	57	2017
Risk Quantification for Policy Deployment PS Thomas, G Theocharous, M Ghavamzadeh US Patent App. 14/552,047, 2016	57	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	54	2019

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