Towards Measuring Similarity in Description Logics. - Alexander Borgida; Thomas J. Walsh 001; Haym Hirsh (Description Logics, 2005)

Efficient Exploration With Latent Structure. - Bethany R. Leffler; Michael L. Littman; Alexander L. Strehl; Thomas J. Walsh 001 (Robotics: Science and Systems, 2005)

Challenges in Securing Voice over IP. - Thomas J. Walsh 001; D. Richard Kuhn (IEEE Secur. Priv., 2005)

Towards a Unified Theory of State Abstraction for MDPs. - Lihong Li 001; Thomas J. Walsh 001; Michael L. Littman (AI&M, 2006)

A Multiple Representation Approach to Learning Dynamical Systems. - Thomas J. Walsh 001; Michael L. Littman (AAAI Fall Symposium: Computational Approaches to Representation Change during Learning and Development, 2007)

Planning and Learning in Environments with Delayed Feedback. - Thomas J. Walsh 001; Ali Nouri; Lihong Li 001; Michael L. Littman (ECML, 2007)

Efficient Learning of Action Schemas and Web-Service Descriptions. - Thomas J. Walsh 001; Michael L. Littman (AAAI, 2008)

Knows what it knows: a framework for self-aware learning. - Lihong Li 001; Michael L. Littman; Thomas J. Walsh 001 (ICML, 2008)

Exploring compact reinforcement-learning representations with linear regression. - Thomas J. Walsh 001; Istvan Szita; Carlos Diuk; Michael L. Littman (UAI, 2009)

Learning and planning in environments with delayed feedback. - Thomas J. Walsh 001; Ali Nouri; Lihong Li 001; Michael L. Littman (Auton. Agents Multi Agent Syst., 2009)

Learning Lexicographic Preference Models. - Fusun Yaman; Thomas J. Walsh 001; Michael L. Littman; Marie desJardins (Preference Learning, 2010)

Integrating Sample-Based Planning and Model-Based Reinforcement Learning. - Thomas J. Walsh 001; Sergiu Goschin; Michael L. Littman (AAAI, 2010)

Generalizing Apprenticeship Learning across Hypothesis Classes. - Thomas J. Walsh 001; Kaushik Subramanian; Michael L. Littman; Carlos Diuk (ICML, 2010)

Leadership Games and their Application in Super-Peer Networks. - Thomas J. Walsh 001; Javad Taheri; Jeremy B. Wright; Paul R. Cohen (Applied Adversarial Reasoning and Risk Modeling, 2011)

A Framework for Teaching and Executing Verb Phrases. - Daniel Hewlett; Thomas J. Walsh 001; Paul R. Cohen (AAAI Spring Symposium: Help Me Help You: Bridging the Gaps in Human-Agent Collaboration, 2011)

Human Natural Instruction of a Simulated Electronic Student. - Tasneem Kaochar; Raquel Torres Peralta; Clayton T. Morrison; Thomas J. Walsh 001; Ian R. Fasel; Sumin Beyon; Anh Tran; Jeremy B. Wright; Paul R. Cohen (AAAI Spring Symposium: Help Me Help You: Bridging the Gaps in Human-Agent Collaboration, 2011)

Learning a Skill-Teaching Curriculum with Dynamic Bayes Nets. - Derek T. Green; Thomas J. Walsh 001; Paul R. Cohen; Yu-Han Chang (IAAI, 2011)

Teaching and executing verb phrases. - Daniel Hewlett; Thomas J. Walsh 001; Paul R. Cohen (ICDL-EPIROB, 2011)

Blending Autonomous Exploration and Apprenticeship Learning. - Thomas J. Walsh 001; Daniel Hewlett; Clayton T. Morrison (NIPS, 2011)

Challenges to decoding the intention behind natural instruction. - Raquel Torres Peralta; Tasneem Kaochar; Ian R. Fasel; Clayton T. Morrison; Thomas J. Walsh 001; Paul R. Cohen (RO-MAN, 2011)

Gender Differences and the Value of Choice in Intelligent Tutoring Systems. - Derek T. Green; Thomas J. Walsh 001; Paul R. Cohen; Carole R. Beal; Yu-Han Chang (UMAP, 2011)

Towards Understanding How Humans Teach Robots. - Tasneem Kaochar; Raquel Torres Peralta; Clayton T. Morrison; Ian R. Fasel; Thomas J. Walsh 001; Paul R. Cohen (UMAP, 2011)

Democratic approximation of lexicographic preference models. - Fusun Yaman; Thomas J. Walsh 001; Michael L. Littman; Marie desJardins (Artif. Intell., 2011)

Knows what it knows: a framework for self-aware learning. - Lihong Li 001; Michael L. Littman; Thomas J. Walsh 001; Alexander L. Strehl (Mach. Learn., 2011)

Dynamic Teaching in Sequential Decision Making Environments. - Thomas J. Walsh 001; Sergiu Goschin (UAI, 2012)

Learning web-service task descriptions from traces. - Thomas J. Walsh 001; Michael L. Littman; Alexander Borgida (Web Intell. Agent Syst., 2012)

Batch-iFDD for Representation Expansion in Large MDPs. - Alborz Geramifard; Thomas J. Walsh 001; Nicholas Roy; Jonathan P. How (UAI, 2013)

A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning. - Alborz Geramifard; Thomas J. Walsh 001; Stefanie Tellex; Girish Chowdhary 001; Nicholas Roy; Jonathan P. How (Found. Trends Mach. Learn., 2013)

Sample Efficient Reinforcement Learning with Gaussian Processes. - Robert C. Grande; Thomas J. Walsh 001; Jonathan P. How (ICML, 2014)

Reinforcement learning with multi-fidelity simulators. - Mark Cutler; Thomas J. Walsh 001; Jonathan P. How (ICRA, 2014)

Special Issue on Aerospace and Mechanical Applications of Reinforcement Learning and Adaptive Learning Based Control. - Jonathan P. How; Girish Chowdhary 001; Thomas J. Walsh 001 (J. Aerosp. Inf. Syst., 2014)

Real-World Reinforcement Learning via Multifidelity Simulators. - Mark Cutler; Thomas J. Walsh 001; Jonathan P. How (IEEE Trans. Robotics, 2015)

Bayesian Nonparametric Reward Learning From Demonstration. - Bernard Michini; Thomas J. Walsh 001; Ali-akbar Agha-mohammadi; Jonathan P. How (IEEE Trans. Robotics, 2015)

Online Regression for Data With Changepoints Using Gaussian Processes and Reusable Models. - Robert C. Grande; Thomas J. Walsh 001; Girish Chowdhary 001; Sarah Ferguson; Jonathan P. How (IEEE Trans. Neural Networks Learn. Syst., 2017)

Outracing champion Gran Turismo drivers with deep reinforcement learning. - Peter R. Wurman; Samuel Barrett; Kenta Kawamoto; James MacGlashan; Kaushik Subramanian; Thomas J. Walsh 001; Roberto Capobianco; Alisa Devlic; Franziska Eckert; Florian Fuchs 004; Leilani Gilpin; Piyush Khandelwal; et al. (Nat., 2022)

Composing Efficient, Robust Tests for Policy Selection. - Dustin Morrill; Thomas J. Walsh 001; Daniel Hernandez; Peter R. Wurman; Peter Stone 001 (UAI, 2023)

Event Tables for Efficient Experience Replay. - Varun Raj Kompella; Thomas Walsh 001; Samuel Barrett; Peter R. Wurman; Peter Stone 001 (Trans. Mach. Learn. Res., 2023)