P. Thomas

发表

Increasing the Action Gap: New Operators for Reinforcement Learning

Marc G. Bellemare, Rémi Munos, Philip S. Thomas, 2015, AAAI.

Bias in Natural Actor-Critic Algorithms

Philip Thomas, P. Thomas, 2014, ICML.

TD γ : Re-evaluating Complex Backups in Temporal Difference Learning

P. Thomas, G. Konidaris, 2011 .

Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces pdf

Bo Liu, Ji Liu, Sridhar Mahadevan, 2014, ArXiv.

Motor primitive discovery

Andrew G. Barto, Philip S. Thomas, A. Barto, 2012, 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL).

Policy Gradient Coagent Networks

Philip S. Thomas, P. Thomas, 2011, NIPS.

Conjugate Markov Decision Processes

Andrew G. Barto, Philip S. Thomas, A. Barto, 2011, ICML.

Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning pdf

Philip S. Thomas, Emma Brunskill, P. Thomas, 2016, ICML.

Value Function Approximation in Reinforcement Learning Using the Fourier Basis

George Konidaris, Philip S. Thomas, Sarah Osentoski, 2011, AAAI.

Personalized Ad Recommendation Systems for Life-Time Value Optimization with Guarantees

Philip S. Thomas, Mohammad Ghavamzadeh, Georgios Theocharous, 2015, IJCAI.

High-Confidence Off-Policy Evaluation

Philip S. Thomas, Mohammad Ghavamzadeh, Georgios Theocharous, 2015, AAAI.

Training an Actor-Critic Reinforcement Learning Controller for Arm Movement Using Human-Generated Rewards

Philip S. Thomas, Kathleen M. Jagodnik, Robert F. Kirsch, 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Application of the Actor-Critic Architecture to Functional Electrical Stimulation Control of a Human Arm

Philip S. Thomas, Kathleen M. Jagodnik, Antonie J. van den Bogert, 2009, IAAI.

Ad Recommendation Systems for Life-Time Value Optimization

Philip S. Thomas, Mohammad Ghavamzadeh, Georgios Theocharous, 2015, WWW.

Predictive Off-Policy Policy Evaluation for Nonstationary Decision Problems, with Applications to Digital Marketing

Philip S. Thomas, Mohammad Ghavamzadeh, Georgios Theocharous, 2017, AAAI.

Learning Action Representations for Reinforcement Learning pdf

Philip S. Thomas, Georgios Theocharous, James Kostas, 2019, ICML.

On Ensuring that Intelligent Machines Are Well-Behaved pdf

Bruno Castro da Silva, Andrew G. Barto, Philip S. Thomas, 2017, ArXiv.

Decoupling Gradient-Like Learning Rules from Representations

Philip S. Thomas, Christoph Dann, Emma Brunskill, 2018, ICML.

CMPSCI 687 : Reinforcement Learning Fall 2020 Class Syllabus , Notes , and Assignments

P. Thomas, 2019 .

Natural Option Critic

Critic, P. Thomas, Saket Tiwari, 2018, AAAI.

Evaluating the Performance of Reinforcement Learning Algorithms

Scott M. Jordan, P. Thomas, Yash Chandak, 2020, ICML.

FES Control of a Human Arm Using Reinforcement Learning *

Kathleen M. Jagodnik, P. Thomas, M. Branicky, 2022 .

Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines pdf

Philip S. Thomas, Emma Brunskill, P. Thomas, 2017, ArXiv.

Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL

P. Thomas, S. Zilberstein, Abhinav Bhatia, 2022, ArXiv.

Policy Evaluation Using the \Omega -Return

Scott Niekum, George Konidaris, Philip S. Thomas, 2015, NIPS 2015.

Mechanizing Soundness of Off-Policy Evaluation

P. Thomas, Michael Norrish, Jared Yeager, 2022, ITP.

Low Variance Off-policy Evaluation with State-based Importance Sampling

David M. Bossens, P. Thomas, 2022, 2212.03932.

Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms

Philip S. Thomas, Pinar Ozisik, P. Thomas, 2020, NeurIPS.

Universal Off-Policy Evaluation pdf

Bruno Castro da Silva, Scott Niekum, Philip S. Thomas, 2021, NeurIPS.

Decoupling Learning Rules from Representations pdf

Philip S. Thomas, Christoph Dann, Emma Brunskill, 2017, ArXiv.

Concentration Inequalities for Conditional Value at Risk

Philip S. Thomas, Erik G. Learned-Miller, P. Thomas, 2019, ICML.

Offline Contextual Bandits with High Probability Fairness Guarantees

Yuriy Brun, Ari Kobren, Philip S. Thomas, 2019, NeurIPS.

Preventing undesirable behavior of intelligent machines

Yuriy Brun, Philip S Thomas, Bruno Castro da Silva, 2019, Science.

Lifelong Learning with a Changing Action Set pdf

Philip S. Thomas, Georgios Theocharous, Chris Nota, 2019, AAAI.

Reinforcement Learning for Strategic Recommendations pdf

Philip S. Thomas, Frits de Nijs, Georgios Theocharous, 2020, ArXiv.

Fairness Guarantees under Demographic Shift

P. Thomas, S. Niekum, Yuriy Brun, 2022, ICLR.

Reinforcement Learning When All Actions are Not Always Available pdf

Philip S. Thomas, Georgios Theocharous, Yash Chandak, 2019, AAAI.

Optimizing for the Future in Non-Stationary MDPs pdf

Sridhar Mahadevan, Philip S. Thomas, Georgios Theocharous, 2020, ICML.

Importance Sampling for Fair Policy Selection

Philip S. Thomas, Emma Brunskill, Shayan Doroudi, 2017, UAI.

Optimization using Parallel Gradient Evaluations on Multiple Parameters

P. Thomas, Yash Chandak, Shiv Shankar, 2023, arXiv.org.

Edge-Compatible Reinforcement Learning for Recommendations

P. Thomas, Georgios Theocharous, James Kostas, 2021, arXiv.org.

Asynchronous Coagent Networks: Stochastic Networks for Reinforcement Learning without Backpropagation or a Clock

Philip S. Thomas, Chris Nota, James E. Kostas, 2019, ICML.

A Notation for Markov Decision Processes pdf

Philip S. Thomas, P. Thomas, 2015, ArXiv.

Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation

Philip S. Thomas, Emma Brunskill, Zhaohan Guo, 2017, NIPS.

High Confidence Policy Improvement

Philip S. Thomas, Mohammad Ghavamzadeh, Georgios Theocharous, 2015, ICML.

High-Confidence Off-Policy (or Counterfactual) Variance Estimation

Philip S. Thomas, Shiv Shankar, Yash Chandak, 2021, AAAI.

A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning

Philip S. Thomas, Francisco M. Garcia, P. Thomas, 2019, AAMAS.

Towards Safe Policy Improvement for Non-Stationary MDPs

Scott M. Jordan, P. Thomas, Martha White, 2020, NeurIPS.

Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs pdf

Romain Laroche, Joelle Pineau, Philip S. Thomas, 2021, NeurIPS.

Seldonian Toolkit: Building Software with Safe and Fair Machine Learning

James E. Kostas, P. Thomas, Yuriy Brun, 2023, 2023 IEEE/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion).

Enforcing Delayed-Impact Fairness Guarantees

P. Thomas, Yuriy Brun, B. C. Silva, 2022, arXiv.org.

Off-Policy Evaluation for Action-Dependent Non-Stationary Environments

Nathaniel D. Bastian, P. Thomas, Yash Chandak, 2023, NeurIPS.

SOPE: Spectrum of Off-Policy Estimators pdf

Scott Niekum, Philip S. Thomas, Stephen Giguere, 2021, NeurIPS.

Is the Policy Gradient a Gradient? pdf

Philip S. Thomas, Chris Nota, P. Thomas, 2019, AAMAS.

High Confidence Generalization for Reinforcement Learning

Philip S. Thomas, Georgios Theocharous, Scott M. Jordan, 2021, ICML.

Coagent Networks: Generalized and Scaled

Scott M. Jordan, James E. Kostas, P. Thomas, 2023, ArXiv.

Low Variance Off-policy Evaluation with State-based Importance Sampling

David M. Bossens, P. Thomas, 2022, arXiv.org.

Learning Reusable Options for Multi-Task Reinforcement Learning pdf

Philip S. Thomas, Chris Nota, Francisco M. Garcia, 2020, ArXiv.

Reinforcement Learning Without Backpropagation or a Clock

Philip S. Thomas, James Kostas, Chris Nota, 2019 .

A Compression-Inspired Framework for Macro Discovery

Philip S. Thomas, Francisco M. Garcia, Bruno C. da Silva, 2017, AAMAS.

Using Options for Long-Horizon Off-Policy Evaluation pdf

Philip S. Thomas, Emma Brunskill, Zhaohan Daniel Guo, 2017, ArXiv.

Projected Natural Actor-Critic

Sridhar Mahadevan, Philip S. Thomas, Stephen Giguere, 2013, NIPS.

Data-Efficient Policy Evaluation Through Behavior Policy Search

Josiah P. Hanna, P. Thomas, P. Stone, 2017, International Conference on Machine Learning.

Human-Like Rewards to Train a Reinforcement Learning Controller for Planar Arm Movement

Philip S. Thomas, Kathleen M. Jagodnik, Antonie J. van den Bogert, 2016, IEEE Transactions on Human-Machine Systems.

Fairness Guarantees under Demographic Shift

P. Thomas, S. Niekum, Yuriy Brun, 2022, ICLR.

Reinforcement learning with spiking coagents

P. Thomas, Abhishek Sharma, R. Kozma, 2019, ArXiv.

Reinforcement learning with a network of spiking agents

Hananel Hazan, Robert Kozma, Philip S. Thomas, 2019, 1910.06489.

TD_gamma: Re-evaluating Complex Backups in Temporal Difference Learning

Scott Niekum, George Konidaris, Philip S. Thomas, 2011, NIPS.

Creating a Reinforcement Learning Controller for Functional Electrical Stimulation of a Human Arm.

Kathleen M. Jagodnik, Philip S Thomas, Michael Branicky, 2008, The ... Yale Workshop on Adaptive and Learning Systems.

Natural Temporal Difference Learning

Philip S. Thomas, William Dabney, P. Thomas, 2014, AAAI.

GeNGA: A Generalization of Natural Gradient Ascent with Positive and Negative Convergence Results

Philip Thomas, P. Thomas, 2014, ICML.

Importance Sampling with Unequal Support pdf

Philip S. Thomas, Emma Brunskill, P. Thomas, 2016, AAAI.

Some Recent Applications of Reinforcement Learning

R. S. Sutton, P. S. Thomas, R. Sutton, 2017 .