Parr, Ronald

52 publications

NeurIPS 2025 A Unifying View of Linear Function Approximation in Off-Policy RL Through Matrix Splitting and Preconditioning Zechen Wu, Amy Greenwald, Ronald Parr

NeurIPS 2024 Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy Cameron Allen, Aaron Kirtland, Ruo Yu Tao, Sam Lobel, Daniel Scott, Nicholas Petrocelli, Omer Gottesman, Ronald Parr, Michael L. Littman, George Konidaris

ICMLW 2024 Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy Cameron Allen, Aaron T. Kirtland, Ruo Yu Tao, Sam Lobel, Daniel Scott, Nicholas Petrocelli, Omer Gottesman, Ronald Parr, Michael Littman, George Konidaris

ICML 2024 Position: Amazing Things Come from Having Many Good Models Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner

NeurIPS 2024 Using Noise to Infer Aspects of Simplicity Without Learning Zachery Boner, Harry Chen, Lesia Semenova, Ronald Parr, Cynthia Rudin

NeurIPS 2023 A Path to Simpler Models Starts with Noise Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin

ICML 2021 Policy Caches with Successor Features Mark Nemecek, Ronald Parr

AAAI 2016 Distance Minimization for Reward Learning from Scored Trajectories Benjamin Burchfiel, Carlo Tomasi, Ronald Parr

AAAI 2016 Efficient PAC-Optimal Exploration in Concurrent, Continuous State MDPs with Delayed Updates Jason Pazis, Ronald Parr

AAAI 2013 PAC Optimal Exploration in Continuous Space Markov Decision Processes Jason Pazis, Ronald Parr

AAAI 2013 Sample Complexity and Performance Bounds for Non-Parametric Approximate Linear Programming Jason Pazis, Ronald Parr

AAAI 2012 Computing Optimal Strategies to Commit to in Stochastic Games Joshua Letchford, Liam MacDermed, Vincent Conitzer, Ronald Parr, Charles L. Isbell Jr.

ICML 2012 Greedy Algorithms for Sparse Reinforcement Learning Christopher Painter-Wakefield, Ronald Parr

UAI 2012 Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs Gavin Taylor, Ronald Parr

ICML 2011 Generalized Value Functions for Large Action Sets Jason Pazis, Ronald Parr

AAAI 2011 Non-Parametric Approximate Linear Programming for MDPs Jason Pazis, Ronald Parr

IJCAI 2011 Security Games with Multiple Attacker Resources Dmytro Korzhyk, Vincent Conitzer, Ronald Parr

AAAI 2010 Complexity of Computing Optimal Stackelberg Strategies in Security Resource Allocation Games Dmytro Korzhyk, Vincent Conitzer, Ronald Parr

ICML 2010 Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein

NeurIPS 2010 Linear Complementarity for Regularized Policy Evaluation and Improvement Jeffrey Johns, Christopher Painter-wakefield, Ronald Parr

ICML 2009 Kernelized Value Function Approximation for Reinforcement Learning Gavin Taylor, Ronald Parr

IJCAI 2009 Multi-Step Multi-Sensor Hider-Seeker Games Erik Halvorson, Vincent Conitzer, Ronald Parr

ICML 2008 An Analysis of Linear Models, Linear Value-Function Approximation, and Feature Selection for Reinforcement Learning Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield, Michael L. Littman

ICML 2007 Analyzing Feature Generation for Value-Function Approximation Ronald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman

AAAI 2007 Point-Based Policy Iteration Shihao Ji, Ronald Parr, Hui Li, Xuejun Liao, Lawrence Carin

UAI 2007 UAI 2007, Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence, Vancouver, BC, Canada, July 19-22, 2007 Ronald Parr, Linda C. van der Gaag

UAI 2006 Efficient Selection of Disambiguating Actions for Stereo Vision Monika Schaeffer, Ronald Parr

NeurIPS 2005 Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps Austin I. Eliazar, Ronald Parr

ICML 2004 Learning Probabilistic Motion Models for Mobile Robots Austin I. Eliazar, Ronald Parr

IJCAI 2003 Approximate Policy Iteration Using Large-Margin Classifiers Michail G. Lagoudakis, Ronald Parr

IJCAI 2003 DP-SLAM: Fast, Robust Simultaneous Localization and Mapping Without Predetermined Landmarks Austin I. Eliazar, Ronald Parr

JAIR 2003 Efficient Solution Algorithms for Factored MDPs Carlos Guestrin, Daphne Koller, Ronald Parr, Shobha Venkataraman

JMLR 2003 Least-Squares Policy Iteration Michail G. Lagoudakis, Ronald Parr

ICML 2003 Reinforcement Learning as Classification: Leveraging Modern Classifiers Michail G. Lagoudakis, Ronald Parr

ICML 2002 Coordinated Reinforcement Learning Carlos Guestrin, Michail G. Lagoudakis, Ronald Parr

NeurIPS 2002 Learning in Zero-Sum Team Markov Games Using Factored Value Functions Michail G. Lagoudakis, Ronald Parr

UAI 2002 Value Function Approximation in Zero-Sum Markov Games Michail G. Lagoudakis, Ronald Parr

UAI 2001 Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms Uri Lerner, Ronald Parr

IJCAI 2001 Max-Norm Projections for Factored MDPs Carlos Guestrin, Daphne Koller, Ronald Parr

NeurIPS 2001 Model-Free Least-Squares Policy Iteration Michail G. Lagoudakis, Ronald Parr

NeurIPS 2001 Multiagent Planning with Factored MDPs Carlos Guestrin, Daphne Koller, Ronald Parr

AAAI 2000 Bayesian Fault Detection and Diagnosis in Dynamic Systems Uri Lerner, Ronald Parr, Daphne Koller, Gautam Biswas

AAAI 2000 Making Rational Decisions Using Adaptive Utility Elicitation Urszula Chajewska, Daphne Koller, Ronald Parr

UAI 2000 Policy Iteration for Factored MDPs Daphne Koller, Ronald Parr

IJCAI 1999 Computing Factored Value Functions for Policies in Structured MDPs Daphne Koller, Ronald Parr

NeurIPS 1999 Policy Search via Density Estimation Andrew Y. Ng, Ronald Parr, Daphne Koller

NeurIPS 1999 Reinforcement Learning Using Approximate Belief States Andres C. Rodriguez, Ronald Parr, Daphne Koller

UAI 1998 Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems Ronald Parr

NeurIPS 1997 Generalized Prioritized Sweeping David Andre, Nir Friedman, Ronald Parr

NeurIPS 1997 Reinforcement Learning with Hierarchies of Machines Ronald Parr, Stuart J. Russell

IJCAI 1995 Approximating Optimal Policies for Partially Observable Stochastic Domains Ronald Parr, Stuart Russell

IJCAI 1993 Provably Bounded Optimal Agents Stuart J. Russell, Devika Subramanian, Ronald Parr