Philip S. Thomas

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 80

Top Topics

Temporal Difference

Policy Gradient

Reinforcement Learning

Policy Evaluation

Top Venues

Publications

Scott M. Jordan, Adam White, Bruno Castro da Silva, Martha White, Philip S. Thomas
Position: Benchmarking is Limited in Reinforcement Learning Research. CoRR (2024)
Dhawal Gupta, Scott M. Jordan, Shreyas Chaudhari, Bo Liu, Philip S. Thomas, Bruno Castro da Silva
From Past to Future: Rethinking Eligibility Traces. AAAI (2024)
Kartik Choudhary, Dhawal Gupta, Philip S. Thomas
ICU-Sepsis: A Benchmark MDP Built from Real Medical Data. CoRR (2024)
Min-Hsuan Yeh, Blossom Metevier, Austin Hoag, Philip S. Thomas
Analyzing the Relationship Between Difference and Ratio-Based Fairness Metrics. FAccT (2024)
James E. Kostas, Scott M. Jordan, Yash Chandak, Georgios Theocharous, Dhawal Gupta, Martha White, Bruno Castro da Silva, Philip S. Thomas
Coagent Networks: Generalized and Scaled. CoRR (2023)
Vincent Liu, Yash Chandak, Philip S. Thomas, Martha White
Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments. CoRR (2023)
Yash Chandak, Shiv Shankar, Nathaniel D. Bastian, Bruno Castro da Silva, Emma Brunskill, Philip S. Thomas
Off-Policy Evaluation for Action-Dependent Non-Stationary Environments. CoRR (2023)
Dhawal Gupta, Yash Chandak, Scott M. Jordan, Philip S. Thomas, Bruno C. da Silva
Behavior Alignment via Reward Function Optimization. NeurIPS (2023)
Austin Hoag, James E. Kostas, Bruno Castro da Silva, Philip S. Thomas, Yuriy Brun
Seldonian Toolkit: Building Software with Safe and Fair Machine Learning. ICSE Companion (2023)
Vincent Liu, Yash Chandak, Philip S. Thomas, Martha White
Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments. AISTATS (2023)
Yash Chandak, Shiv Shankar, Venkata Gandikota, Philip S. Thomas, Arya Mazumdar
Optimization using Parallel Gradient Evaluations on Multiple Parameters. CoRR (2023)
Dhawal Gupta, Scott M. Jordan, Shreyas Chaudhari, Bo Liu, Philip S. Thomas, Bruno Castro da Silva
From Past to Future: Rethinking Eligibility Traces. CoRR (2023)
Yuhong Luo, Austin Hoag, Philip S. Thomas
Learning Fair Representations with High-Confidence Guarantees. CoRR (2023)
Dhawal Gupta, Yash Chandak, Scott M. Jordan, Philip S. Thomas, Bruno Castro da Silva
Behavior Alignment via Reward Function Optimization. CoRR (2023)
Aline Weber, Blossom Metevier, Yuriy Brun, Philip S. Thomas, Bruno Castro da Silva
Enforcing Delayed-Impact Fairness Guarantees. CoRR (2022)
Yash Chandak, Shiv Shankar, Nathaniel D. Bastian, Bruno C. da Silva, Emma Brunskill, Philip S. Thomas
Off-Policy Evaluation for Action-Dependent Non-stationary Environments. NeurIPS (2022)
Stephen Giguere, Blossom Metevier, Bruno Castro da Silva, Yuriy Brun, Philip S. Thomas, Scott Niekum
Fairness Guarantees under Demographic Shift. ICLR (2022)
Abhinav Bhatia, Philip S. Thomas, Shlomo Zilberstein
Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL. CoRR (2022)
Jared Yeager, J. Eliot B. Moss, Michael Norrish, Philip S. Thomas
Mechanizing Soundness of Off-Policy Evaluation. ITP (2022)
Harsh Satija, Philip S. Thomas, Joelle Pineau, Romain Laroche
Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs. NeurIPS (2021)
Christina J. Yuan, Yash Chandak, Stephen Giguere, Philip S. Thomas, Scott Niekum
SOPE: Spectrum of Off-Policy Estimators. CoRR (2021)
Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik G. Learned-Miller, Emma Brunskill, Philip S. Thomas
Universal Off-Policy Evaluation. CoRR (2021)
Christina J. Yuan, Yash Chandak, Stephen Giguere, Philip S. Thomas, Scott Niekum
SOPE: Spectrum of Off-Policy Estimators. NeurIPS (2021)
Yash Chandak, Shiv Shankar, Philip S. Thomas
High-Confidence Off-Policy (or Counterfactual) Variance Estimation. AAAI (2021)
Harsh Satija, Philip S. Thomas, Joelle Pineau, Romain Laroche
Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs. CoRR (2021)
Yash Chandak, Shiv Shankar, Philip S. Thomas
High-Confidence Off-Policy (or Counterfactual) Variance Estimation. CoRR (2021)
James E. Kostas, Philip S. Thomas, Georgios Theocharous
Edge-Compatible Reinforcement Learning for Recommendations. CoRR (2021)
James Kostas, Yash Chandak, Scott M. Jordan, Georgios Theocharous, Philip S. Thomas
High Confidence Generalization for Reinforcement Learning. ICML (2021)
Ali Montazeralghaem, James Allan, Philip S. Thomas
Large-scale Interactive Conversational Recommendation System using Actor-Critic Framework. RecSys (2021)
Dhawal Gupta, Gabor Mihucz, Matthew Schlegel, James E. Kostas, Philip S. Thomas, Martha White
Structural Credit Assignment in Neural Networks using Reinforcement Learning. NeurIPS (2021)
Yash Chandak, Scott Niekum, Bruno C. da Silva, Erik G. Learned-Miller, Emma Brunskill, Philip S. Thomas
Universal Off-Policy Evaluation. NeurIPS (2021)
Yash Chandak, Georgios Theocharous, Blossom Metevier, Philip S. Thomas
Reinforcement Learning When All Actions Are Not Always Available. AAAI (2020)
Yash Chandak, Scott M. Jordan, Georgios Theocharous, Martha White, Philip S. Thomas
Towards Safe Policy Improvement for Non-Stationary MDPs. CoRR (2020)
James Kostas, Chris Nota, Philip S. Thomas
Asynchronous Coagent Networks. ICML (2020)
Yash Chandak, Georgios Theocharous, Shiv Shankar, Martha White, Sridhar Mahadevan, Philip S. Thomas
Optimizing for the Future in Non-Stationary MDPs. CoRR (2020)
Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas
Evaluating the Performance of Reinforcement Learning Algorithms. CoRR (2020)
Yash Chandak, Georgios Theocharous, Chris Nota, Philip S. Thomas
Lifelong Learning with a Changing Action Set. AAAI (2020)
Pinar Ozisik, Philip S. Thomas
Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms. NeurIPS (2020)
Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas
Evaluating the Performance of Reinforcement Learning Algorithms. ICML (2020)
Georgios Theocharous, Yash Chandak, Philip S. Thomas, Frits de Nijs
Reinforcement Learning for Strategic Recommendations. CoRR (2020)
Francisco M. Garcia, Chris Nota, Philip S. Thomas
Learning Reusable Options for Multi-Task Reinforcement Learning. CoRR (2020)
Yash Chandak, Scott M. Jordan, Georgios Theocharous, Martha White, Philip S. Thomas
Towards Safe Policy Improvement for Non-Stationary MDPs. NeurIPS (2020)
Chris Nota, Philip S. Thomas
Is the Policy Gradient a Gradient? AAMAS (2020)
Yash Chandak, Georgios Theocharous, Shiv Shankar, Martha White, Sridhar Mahadevan, Philip S. Thomas
Optimizing for the Future in Non-Stationary MDPs. ICML (2020)
Philip S. Thomas, Scott M. Jordan, Yash Chandak, Chris Nota, James Kostas
Classical Policy Gradient: Preserving Bellman's Principle of Optimality. CoRR (2019)
Tengyang Xie, Philip S. Thomas, Gerome Miklau
Privacy Preserving Off-Policy Evaluation. CoRR (2019)
Yash Chandak, Georgios Theocharous, James Kostas, Scott M. Jordan, Philip S. Thomas
Learning Action Representations for Reinforcement Learning. CoRR (2019)
Erik G. Learned-Miller, Philip S. Thomas
A New Confidence Interval for the Mean of a Bounded Random Variable. CoRR (2019)
Chris Nota, Philip S. Thomas
Is the Policy Gradient a Gradient? CoRR (2019)
Philip S. Thomas, Erik G. Learned-Miller
Concentration Inequalities for Conditional Value at Risk. ICML (2019)
James Kostas, Chris Nota, Philip S. Thomas
Asynchronous Coagent Networks: Stochastic Networks for Reinforcement Learning without Backpropagation or a Clock. CoRR (2019)
Saket Tiwari, Philip S. Thomas
Natural Option Critic. AAAI (2019)
Francisco M. Garcia, Philip S. Thomas
A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning. CoRR (2019)
Yash Chandak, Georgios Theocharous, Blossom Metevier, Philip S. Thomas
Reinforcement Learning When All Actions are Not Always Available. CoRR (2019)
Yash Chandak, Georgios Theocharous, Chris Nota, Philip S. Thomas
Lifelong Learning with a Changing Action Set. CoRR (2019)
Yash Chandak, Georgios Theocharous, James Kostas, Scott M. Jordan, Philip S. Thomas
Learning Action Representations for Reinforcement Learning. ICML (2019)
Sneha Aenugu, Abhishek Sharma, Sasikiran Yelamarthi, Hananel Hazan, Philip S. Thomas, Robert Kozma
Reinforcement learning with spiking coagents. CoRR (2019)
Francisco M. Garcia, Philip S. Thomas
A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning. NeurIPS (2019)
Francisco M. Garcia, Philip S. Thomas
A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning. AAMAS (2019)
Blossom Metevier, Stephen Giguere, Sarah Brockman, Ari Kobren, Yuriy Brun, Emma Brunskill, Philip S. Thomas
Offline Contextual Bandits with High Probability Fairness Guarantees. NeurIPS (2019)
Francisco M. Garcia, Bruno C. da Silva, Philip S. Thomas
A Compression-Inspired Framework for Macro Discovery. AAMAS (2019)
Philip S. Thomas, Christoph Dann, Emma Brunskill
Decoupling Gradient-Like Learning Rules from Representations. ICML (2018)
Shayan Doroudi, Philip S. Thomas, Emma Brunskill
Importance Sampling for Fair Policy Selection. IJCAI (2018)
Saket Tiwari, Philip S. Thomas
Natural Option Critic. CoRR (2018)
Philip S. Thomas, Christoph Dann, Emma Brunskill
Decoupling Learning Rules from Representations. CoRR (2017)
Philip S. Thomas, Emma Brunskill
Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines. CoRR (2017)
Zhaohan Guo, Philip S. Thomas, Emma Brunskill
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation. NIPS (2017)
Josiah P. Hanna, Philip S. Thomas, Peter Stone, Scott Niekum
Data-Efficient Policy Evaluation Through Behavior Policy Search. ICML (2017)
Philip S. Thomas, Emma Brunskill
Importance Sampling with Unequal Support. AAAI (2017)
Philip S. Thomas, Bruno Castro da Silva, Andrew G. Barto, Emma Brunskill
On Ensuring that Intelligent Machines Are Well-Behaved. CoRR (2017)
Shayan Doroudi, Philip S. Thomas, Emma Brunskill
Importance Sampling for Fair Policy Selection. UAI (2017)
Josiah P. Hanna, Philip S. Thomas, Peter Stone, Scott Niekum
Data-Efficient Policy Evaluation Through Behavior Policy Search. CoRR (2017)
Zhaohan Daniel Guo, Philip S. Thomas, Emma Brunskill
Using Options for Long-Horizon Off-Policy Evaluation. CoRR (2017)
Philip S. Thomas, Georgios Theocharous, Mohammad Ghavamzadeh, Ishan Durugkar, Emma Brunskill
Predictive Off-Policy Policy Evaluation for Nonstationary Decision Problems, with Applications to Digital Marketing. AAAI (2017)
Marc G. Bellemare, Georg Ostrovski, Arthur Guez, Philip S. Thomas, Rémi Munos
Increasing the Action Gap: New Operators for Reinforcement Learning. AAAI (2016)
Philip S. Thomas, Emma Brunskill
Importance Sampling with Unequal Support. CoRR (2016)
Philip S. Thomas, Emma Brunskill
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning. ICML (2016)
Philip S. Thomas, Emma Brunskill
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning. CoRR (2016)
Kathleen M. Jagodnik, Philip S. Thomas, Antonie J. van den Bogert, Michael S. Branicky, Robert F. Kirsch
Human-Like Rewards to Train a Reinforcement Learning Controller for Planar Arm Movement. IEEE Trans. Hum. Mach. Syst. 46 (5) (2016)
Philip S. Thomas, Bruno Castro da Silva, Christoph Dann, Emma Brunskill
Energetic Natural Gradient Descent. ICML (2016)
Philip S. Thomas, Scott Niekum, Georgios Theocharous, George Dimitri Konidaris
Policy Evaluation Using the Ω-Return. NIPS (2015)
Marc G. Bellemare, Georg Ostrovski, Arthur Guez, Philip S. Thomas, Rémi Munos
Increasing the Action Gap: New Operators for Reinforcement Learning. CoRR (2015)
Philip S. Thomas, Georgios Theocharous, Mohammad Ghavamzadeh
High-Confidence Off-Policy Evaluation. AAAI (2015)
Georgios Theocharous, Philip S. Thomas, Mohammad Ghavamzadeh
Personalized Ad Recommendation Systems for Life-Time Value Optimization with Guarantees. IJCAI (2015)
Philip S. Thomas
A Notation for Markov Decision Processes. CoRR (2015)
Philip S. Thomas, Georgios Theocharous, Mohammad Ghavamzadeh
High Confidence Policy Improvement. ICML (2015)
Georgios Theocharous, Philip S. Thomas, Mohammad Ghavamzadeh
Ad Recommendation Systems for Life-Time Value Optimization. WWW (Companion Volume) (2015)
William Dabney, Philip S. Thomas
Natural Temporal Difference Learning. AAAI (2014)
Sridhar Mahadevan, Bo Liu, Philip S. Thomas, William Dabney, Stephen Giguere, Nicholas Jacek, Ian Gemp, Ji Liu
Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces. CoRR (2014)
Philip S. Thomas, William Dabney, Stephen Giguere, Sridhar Mahadevan
Projected Natural Actor-Critic. NIPS (2013)
Philip S. Thomas, Andrew G. Barto
Motor primitive discovery. ICDL-EPIROB (2012)
George Dimitri Konidaris, Scott Niekum, Philip S. Thomas
TD_gamma: Re-evaluating Complex Backups in Temporal Difference Learning. NIPS (2011)
Philip S. Thomas
Policy Gradient Coagent Networks. NIPS (2011)
George Dimitri Konidaris, Sarah Osentoski, Philip S. Thomas
Value Function Approximation in Reinforcement Learning Using the Fourier Basis. AAAI (2011)
Philip S. Thomas, Andrew G. Barto
Conjugate Markov Decision Processes. ICML (2011)
Philip S. Thomas, Antonie J. van den Bogert, Kathleen M. Jagodnik, Michael S. Branicky
Application of the Actor-Critic Architecture to Functional Electrical Stimulation Control of a Human Arm. IAAI (2009)