Application of the Actor-Critic Architecture to Functional Electrical Stimulation Control of a Human Arm. - Philip S. Thomas; Antonie J. van den Bogert; Kathleen M. Jagodnik; Michael S. Branicky (IAAI, 2009)

Value Function Approximation in Reinforcement Learning Using the Fourier Basis. - George Dimitri Konidaris; Sarah Osentoski; Philip S. Thomas (AAAI, 2011)

Conjugate Markov Decision Processes. - Philip S. Thomas; Andrew G. Barto (ICML, 2011)

TD_gamma: Re-evaluating Complex Backups in Temporal Difference Learning. - George Dimitri Konidaris; Scott Niekum; Philip S. Thomas (NIPS, 2011)

Policy Gradient Coagent Networks. - Philip S. Thomas (NIPS, 2011)

Motor primitive discovery. - Philip S. Thomas; Andrew G. Barto (ICDL-EPIROB, 2012)

Projected Natural Actor-Critic. - Philip S. Thomas; William Dabney; Stephen Giguere 001; Sridhar Mahadevan (NIPS, 2013)

Natural Temporal Difference Learning. - William Dabney; Philip S. Thomas (AAAI, 2014)

Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces. - Sridhar Mahadevan; Bo Liu 006; Philip S. Thomas; William Dabney; Stephen Giguere 001; Nicholas Jacek; Ian Gemp; Ji Liu 002 (CoRR, 2014)

High-Confidence Off-Policy Evaluation. - Philip S. Thomas; Georgios Theocharous; Mohammad Ghavamzadeh (AAAI, 2015)

High Confidence Policy Improvement. - Philip S. Thomas; Georgios Theocharous; Mohammad Ghavamzadeh (ICML, 2015)

Personalized Ad Recommendation Systems for Life-Time Value Optimization with Guarantees. - Georgios Theocharous; Philip S. Thomas; Mohammad Ghavamzadeh (IJCAI, 2015)

Policy Evaluation Using the Ω-Return. - Philip S. Thomas; Scott Niekum; Georgios Theocharous; George Dimitri Konidaris (NIPS, 2015)

Ad Recommendation Systems for Life-Time Value Optimization. - Georgios Theocharous; Philip S. Thomas; Mohammad Ghavamzadeh (WWW (Companion Volume), 2015)

A Notation for Markov Decision Processes. - Philip S. Thomas (CoRR, 2015)

Increasing the Action Gap: New Operators for Reinforcement Learning. - Marc G. Bellemare; Georg Ostrovski; Arthur Guez; Philip S. Thomas; Rémi Munos (AAAI, 2016)

Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning. - Philip S. Thomas; Emma Brunskill (ICML, 2016)

Energetic Natural Gradient Descent. - Philip S. Thomas; Bruno Castro da Silva; Christoph Dann; Emma Brunskill (ICML, 2016)

Human-Like Rewards to Train a Reinforcement Learning Controller for Planar Arm Movement. - Kathleen M. Jagodnik; Philip S. Thomas; Antonie J. van den Bogert; Michael S. Branicky; Robert F. Kirsch (IEEE Trans. Hum. Mach. Syst., 2016)

Importance Sampling with Unequal Support. - Philip S. Thomas; Emma Brunskill (AAAI, 2017)

Predictive Off-Policy Policy Evaluation for Nonstationary Decision Problems, with Applications to Digital Marketing. - Philip S. Thomas; Georgios Theocharous; Mohammad Ghavamzadeh; Ishan Durugkar; Emma Brunskill (AAAI, 2017)

Data-Efficient Policy Evaluation Through Behavior Policy Search. - Josiah P. Hanna; Philip S. Thomas; Peter Stone 001; Scott Niekum (ICML, 2017)

Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation. - Zhaohan Guo; Philip S. Thomas; Emma Brunskill (NIPS, 2017)

Using Options for Long-Horizon Off-Policy Evaluation. - Zhaohan Daniel Guo; Philip S. Thomas; Emma Brunskill (CoRR, 2017)

Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines. - Philip S. Thomas; Emma Brunskill (CoRR, 2017)

Decoupling Learning Rules from Representations. - Philip S. Thomas; Christoph Dann; Emma Brunskill (CoRR, 2017)

On Ensuring that Intelligent Machines Are Well-Behaved. - Philip S. Thomas; Bruno Castro da Silva; Andrew G. Barto; Emma Brunskill (CoRR, 2017)

Decoupling Gradient-Like Learning Rules from Representations. - Philip S. Thomas; Christoph Dann; Emma Brunskill (ICML, 2018)

Importance Sampling for Fair Policy Selection. - Shayan Doroudi; Philip S. Thomas; Emma Brunskill (IJCAI, 2018)

Natural Option Critic. - Saket Tiwari; Philip S. Thomas (AAAI, 2019)

A Compression-Inspired Framework for Macro Discovery. - Francisco M. Garcia; Bruno C. da Silva 001; Philip S. Thomas (AAMAS, 2019)

Learning Action Representations for Reinforcement Learning. - Yash Chandak; Georgios Theocharous; James E. Kostas; Scott M. Jordan; Philip S. Thomas (ICML, 2019)

Concentration Inequalities for Conditional Value at Risk. - Philip S. Thomas; Erik G. Learned-Miller (ICML, 2019)

A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning. - Francisco M. Garcia; Philip S. Thomas (NeurIPS, 2019)

Offline Contextual Bandits with High Probability Fairness Guarantees. - Blossom Metevier; Stephen Giguere 001; Sarah Brockman; Ari Kobren; Yuriy Brun; Emma Brunskill; Philip S. Thomas (NeurIPS, 2019)

Privacy Preserving Off-Policy Evaluation. - Tengyang Xie; Philip S. Thomas; Gerome Miklau (CoRR, 2019)

Asynchronous Coagent Networks: Stochastic Networks for Reinforcement Learning without Backpropagation or a Clock. - James E. Kostas; Chris Nota; Philip S. Thomas (CoRR, 2019)

A New Confidence Interval for the Mean of a Bounded Random Variable. - Erik G. Learned-Miller; Philip S. Thomas (CoRR, 2019)

Classical Policy Gradient: Preserving Bellman's Principle of Optimality. - Philip S. Thomas; Scott M. Jordan; Yash Chandak; Chris Nota; James E. Kostas (CoRR, 2019)

Reinforcement learning with spiking coagents. - Sneha Aenugu; Abhishek Sharma; Sasikiran Yelamarthi; Hananel Hazan; Philip S. Thomas; Robert Kozma 001 (CoRR, 2019)

Reinforcement Learning When All Actions Are Not Always Available. - Yash Chandak; Georgios Theocharous; Blossom Metevier; Philip S. Thomas (AAAI, 2020)

Lifelong Learning with a Changing Action Set. - Yash Chandak; Georgios Theocharous; Chris Nota; Philip S. Thomas (AAAI, 2020)

Is the Policy Gradient a Gradient? - Chris Nota; Philip S. Thomas (AAMAS, 2020)

Optimizing for the Future in Non-Stationary MDPs. - Yash Chandak; Georgios Theocharous; Shiv Shankar; Martha White; Sridhar Mahadevan; Philip S. Thomas (ICML, 2020)

Evaluating the Performance of Reinforcement Learning Algorithms. - Scott M. Jordan; Yash Chandak; Daniel Cohen; Mengxue Zhang; Philip S. Thomas (ICML, 2020)

Asynchronous Coagent Networks. - James E. Kostas; Chris Nota; Philip S. Thomas (ICML, 2020)

Towards Safe Policy Improvement for Non-Stationary MDPs. - Yash Chandak; Scott M. Jordan; Georgios Theocharous; Martha White; Philip S. Thomas (NeurIPS, 2020)

Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms. - Pinar Ozisik; Philip S. Thomas (NeurIPS, 2020)

Learning Reusable Options for Multi-Task Reinforcement Learning. - Francisco M. Garcia; Chris Nota; Philip S. Thomas (CoRR, 2020)

Reinforcement Learning for Strategic Recommendations. - Georgios Theocharous; Yash Chandak; Philip S. Thomas; Frits de Nijs (CoRR, 2020)

High-Confidence Off-Policy (or Counterfactual) Variance Estimation. - Yash Chandak; Shiv Shankar; Philip S. Thomas (AAAI, 2021)

High Confidence Generalization for Reinforcement Learning. - James E. Kostas; Yash Chandak; Scott M. Jordan; Georgios Theocharous; Philip S. Thomas (ICML, 2021)

Posterior Value Functions: Hindsight Baselines for Policy Gradient Methods. - Chris Nota; Philip S. Thomas; Bruno C. da Silva 001 (ICML, 2021)

Towards Practical Mean Bounds for Small Samples. - My Phan; Philip S. Thomas; Erik G. Learned-Miller (ICML, 2021)

Universal Off-Policy Evaluation. - Yash Chandak; Scott Niekum; Bruno C. da Silva 001; Erik G. Learned-Miller; Emma Brunskill; Philip S. Thomas (NeurIPS, 2021)

Structural Credit Assignment in Neural Networks using Reinforcement Learning. - Dhawal Gupta; Gabor Mihucz; Matthew Schlegel; James E. Kostas; Philip S. Thomas; Martha White (NeurIPS, 2021)

Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs. - Harsh Satija; Philip S. Thomas; Joelle Pineau; Romain Laroche (NeurIPS, 2021)

SOPE: Spectrum of Off-Policy Estimators. - Christina J. Yuan; Yash Chandak; Stephen Giguere 001; Philip S. Thomas; Scott Niekum (NeurIPS, 2021)

Large-scale Interactive Conversational Recommendation System using Actor-Critic Framework. - Ali Montazeralghaem; James Allan; Philip S. Thomas (RecSys, 2021)

Edge-Compatible Reinforcement Learning for Recommendations. - James E. Kostas; Philip S. Thomas; Georgios Theocharous (CoRR, 2021)

Fairness Guarantees under Demographic Shift. - Stephen Giguere 001; Blossom Metevier; Bruno Castro da Silva; Yuriy Brun; Philip S. Thomas; Scott Niekum (ICLR, 2022)

Mechanizing Soundness of Off-Policy Evaluation. - Jared Yeager; J. Eliot B. Moss; Michael Norrish; Philip S. Thomas (ITP, 2022)

Off-Policy Evaluation for Action-Dependent Non-stationary Environments. - Yash Chandak; Shiv Shankar; Nathaniel D. Bastian; Bruno C. da Silva 001; Emma Brunskill; Philip S. Thomas (NeurIPS, 2022)

Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL. - Abhinav Bhatia; Philip S. Thomas; Shlomo Zilberstein (CoRR, 2022)

Enforcing Delayed-Impact Fairness Guarantees. - Aline Weber; Blossom Metevier; Yuriy Brun; Philip S. Thomas; Bruno Castro da Silva (CoRR, 2022)

Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments. - Vincent Liu; Yash Chandak; Philip S. Thomas; Martha White (AISTATS, 2023)

Seldonian Toolkit: Building Software with Safe and Fair Machine Learning. - Austin Hoag; James E. Kostas; Bruno Castro da Silva; Philip S. Thomas; Yuriy Brun (ICSE Companion, 2023)

Behavior Alignment via Reward Function Optimization. - Dhawal Gupta; Yash Chandak; Scott M. Jordan; Philip S. Thomas; Bruno C. da Silva 001 (NeurIPS, 2023)

Optimization using Parallel Gradient Evaluations on Multiple Parameters. - Yash Chandak; Shiv Shankar; Venkata Gandikota; Philip S. Thomas; Arya Mazumdar (CoRR, 2023)

Coagent Networks: Generalized and Scaled. - James E. Kostas; Scott M. Jordan; Yash Chandak; Georgios Theocharous; Dhawal Gupta; Martha White; Bruno Castro da Silva; Philip S. Thomas (CoRR, 2023)

Learning Fair Representations with High-Confidence Guarantees. - Yuhong Luo; Austin Hoag; Philip S. Thomas (CoRR, 2023)

From Past to Future: Rethinking Eligibility Traces. - Dhawal Gupta; Scott M. Jordan; Shreyas Chaudhari; Bo Liu 006; Philip S. Thomas; Bruno Castro da Silva (AAAI, 2024)

Analyzing the Relationship Between Difference and Ratio-Based Fairness Metrics. - Min-Hsuan Yeh; Blossom Metevier; Austin Hoag; Philip S. Thomas (FAccT, 2024)

Position: Benchmarking is Limited in Reinforcement Learning Research. - Scott M. Jordan; Adam White 001; Bruno Castro da Silva; Martha White; Philip S. Thomas (ICML, 2024)

Abstract Reward Processes: Leveraging State Abstraction for Consistent Off-Policy Evaluation. - Shreyas Chaudhari; Ameet Deshpande; Bruno C. da Silva 001; Philip S. Thomas (NeurIPS, 2024)

ICU-Sepsis: A Benchmark MDP Built from Real Medical Data. - Kartik Choudhary; Dhawal Gupta; Philip S. Thomas (RLJ, 2024)

The Cliff of Overcommitment with Policy Gradient Step Sizes. - Scott M. Jordan; Samuel Neumann; James E. Kostas; Adam White 001; Philip S. Thomas (RLJ, 2024)

Supervised Reward Inference. - Will Schwarzer; Jordan Schneider; Philip S. Thomas; Scott Niekum (CoRR, 2025)

Are Deep Speech Denoising Models Robust to Adversarial Noise? - Will Schwarzer; Philip S. Thomas; Andrea Fanelli; Xiaoyu Liu (CoRR, 2025)

Qualia Optimization. - Philip S. Thomas (CoRR, 2025)

Reinforcement Learning from Human Feedback with High-Confidence Safety Constraints. - Yaswanth Chittepu; Blossom Metevier; Will Schwarzer; Austin Hoag; Scott Niekum; Philip S. Thomas (CoRR, 2025)