Ronald Parr

According to our database, Ronald Parr authored at least 69 papers between 1993 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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

CoRR, 2024

Amazing Things Come From Having Many Good Models.

Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner

CoRR, 2024

An Optimal Tightness Bound for the Simulation Lemma.

Sam Lobel, Ronald Parr

RLJ, 2024

Position: Amazing Things Come From Having Many Good Models.

Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

A Path to Simpler Models Starts With Noise.

Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

On the Existence of Simpler Machine Learning Models.

Lesia Semenova, Cynthia Rudin, Ronald Parr

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

2021

Deep Radial-Basis Value Functions for Continuous Control.

Kavosh Asadi, Neev Parikh, Ronald E. Parr, George Dimitri Konidaris, Michael L. Littman

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Fitted Q-Learning for Relational Domains.

Srijita Das, Sriraam Natarajan, Kaushik Roy, Ronald Parr, Kristian Kersting

CoRR, 2020

Deep RBF Value Functions for Continuous Control.

Kavosh Asadi, Ronald E. Parr, George Dimitri Konidaris, Michael L. Littman

CoRR, 2020

2019

Revisiting the Softmax Bellman Operator: New Benefits and New Perspective.

Zhao Song, Ronald Parr, Lawrence Carin

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Revisiting the Softmax Bellman Operator: Theoretical Properties and Practical Benefits.

Zhao Song, Ronald E. Parr, Lawrence Carin

CoRR, 2018

2016

Linear Feature Encoding for Reinforcement Learning.

Zhao Song, Ronald E. Parr, Xuejun Liao, Lawrence Carin

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Improving PAC Exploration Using the Median Of Means.

Jason Pazis, Ronald Parr, Jonathan P. How

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Efficient PAC-Optimal Exploration in Concurrent, Continuous State MDPs with Delayed Updates.

Jason Pazis, Ronald Parr

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

Distance Minimization for Reward Learning from Scored Trajectories.

Benjamin Burchfiel, Carlo Tomasi, Ronald Parr

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2014

Unsupervised discovery of object classes with a mobile robot.

Julian Mason, Bhaskara Marthi, Ronald Parr

Proceedings of the 2014 IEEE International Conference on Robotics and Automation, 2014

2013

Sample Complexity and Performance Bounds for Non-Parametric Approximate Linear Programming.

Jason Pazis, Ronald Parr

Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013

PAC Optimal Exploration in Continuous Space Markov Decision Processes.

Jason Pazis, Ronald Parr

Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013

2012

Computing Stackelberg strategies in stochastic games.

Joshua Letchford, Liam MacDermed, Vincent Conitzer, Ronald Parr, Charles L. Isbell Jr.

SIGecom Exch., 2012

Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (2007)

Ronald Parr, Linda C. van der Gaag

CoRR, 2012

Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs.

Gavin Taylor, Ronald Parr

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Object disappearance for object discovery.

Julian Mason, Bhaskara Marthi, Ronald Parr

Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012

Greedy Algorithms for Sparse Reinforcement Learning.

Christopher Painter-Wakefield, Ronald Parr

Proceedings of the 29th International Conference on Machine Learning, 2012

Computing Optimal Strategies to Commit to in Stochastic Games.

Joshua Letchford, Liam MacDermed, Vincent Conitzer, Ronald Parr, Charles L. Isbell Jr.

Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2011

Security Games with Multiple Attacker Resources.

Dmytro Korzhyk, Vincent Conitzer, Ronald Parr

Proceedings of the IJCAI 2011, 2011

Textured occupancy grids for monocular localization without features.

Julian Mason, Susanna Ricco, Ronald Parr

Proceedings of the IEEE International Conference on Robotics and Automation, 2011

Generalized Value Functions for Large Action Sets.

Jason Pazis, Ronald Parr

Proceedings of the 28th International Conference on Machine Learning, 2011

Solving Stackelberg games with uncertain observability.

Dmytro Korzhyk, Vincent Conitzer, Ronald Parr

Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), 2011

Non-Parametric Approximate Linear Programming for MDPs.

Jason Pazis, Ronald Parr

Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011

2010

Counting Objects with a Combination of Horizontal and Overhead Sensors.

Erik Halvorson, Ronald Parr

Int. J. Robotics Res., 2010

Linear Complementarity for Regularized Policy Evaluation and Improvement.

Jeffrey Johns, Christopher Painter-Wakefield, Ronald Parr

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes.

Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Complexity of Computing Optimal Stackelberg Strategies in Security Resource Allocation Games.

Dmytro Korzhyk, Vincent Conitzer, Ronald Parr

Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010

2009

Multi-Step Multi-Sensor Hider-Seeker Games.

Erik Halvorson, Vincent Conitzer, Ronald Parr

Proceedings of the IJCAI 2009, 2009

Kernelized value function approximation for reinforcement learning.

Gavin Taylor, Ronald Parr

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

Planning Aims for a Network of Horizontal and Overhead Sensors.

Erik Halvorson, Ronald Parr

Proceedings of the Algorithmic Foundation of Robotics VIII, 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

Proceedings of the Machine Learning, 2008

2007

Nonmyopic Multiaspect Sensing With Partially Observable Markov Decision Processes.

Shihao Ji, Ronald Parr, Lawrence Carin

IEEE Trans. Signal Process., 2007

Analyzing feature generation for value-function approximation.

Ronald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman

Proceedings of the Machine Learning, 2007

Point-Based Policy Iteration.

Shihao Ji, Ronald Parr, Hui Li, Xuejun Liao, Lawrence Carin

Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

2006

Efficient Selection of Disambiguating Actions for Stereo Vision.

Monika Schaeffer, Ronald Parr

Proceedings of the UAI '06, 2006

2005

Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps.

Austin I. Eliazar, Ronald Parr

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

2004

DP-SLAM 2.0.

Austin I. Eliazar, Ronald Parr

Proceedings of the 2004 IEEE International Conference on Robotics and Automation, 2004

Learning probabilistic motion models for mobile robots.

Austin I. Eliazar, Ronald Parr

Proceedings of the Machine Learning, 2004

2003

Least-Squares Policy Iteration.

Michail G. Lagoudakis, Ronald Parr

J. Mach. Learn. Res., 2003

Efficient Solution Algorithms for Factored MDPs.

Carlos Guestrin, Daphne Koller, Ronald Parr, Shobha Venkataraman

J. Artif. Intell. Res., 2003

Approximate Policy Iteration using Large-Margin Classifiers.

Michail G. Lagoudakis, Ronald Parr

Proceedings of the IJCAI-03, 2003

DP-SLAM: Fast, Robust Simultaneous Localization and Mapping Without Predetermined Landmarks.

Austin I. Eliazar, Ronald Parr

Proceedings of the IJCAI-03, 2003

Reinforcement Learning as Classification: Leveraging Modern Classifiers.

Michail G. Lagoudakis, Ronald Parr

Proceedings of the Machine Learning, 2003

2002

XPathLearner: An On-line Self-Tuning Markov Histogram for XML Path Selectivity Estimation.

Lipyeow Lim, Min Wang, Sriram Padmanabhan, Jeffrey Scott Vitter, Ronald Parr

Proceedings of 28th International Conference on Very Large Data Bases, 2002

Value Function Approximation in Zero-Sum Markov Games.

Michail G. Lagoudakis, Ronald Parr

Proceedings of the UAI '02, 2002

Least-Squares Methods in Reinforcement Learning for Control.

Michail G. Lagoudakis, Ronald Parr, Michael L. Littman

Proceedings of the Methods and Applications of Artificial Intelligence, 2002

Learning in Zero-Sum Team Markov Games Using Factored Value Functions.

Michail G. Lagoudakis, Ronald Parr

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Coordinated Reinforcement Learning.

Carlos Guestrin, Michail G. Lagoudakis, Ronald Parr

Proceedings of the Machine Learning, 2002

2001

Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms.

Uri Lerner, Ronald Parr

Proceedings of the UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001

Model-Free Least-Squares Policy Iteration.

Michail G. Lagoudakis, Ronald Parr

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

Multiagent Planning with Factored MDPs.

Carlos Guestrin, Daphne Koller, Ronald Parr

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

Max-norm Projections for Factored MDPs.

Carlos Guestrin, Daphne Koller, Ronald Parr

Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, 2001

2000

Policy Iteration for Factored MDPs.

Daphne Koller, Ronald Parr

Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

Bayesian Fault Detection and Diagnosis in Dynamic Systems.

Uri Lerner, Ronald Parr, Daphne Koller, Gautam Biswas

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on on Innovative Applications of Artificial Intelligence, July 30, 2000

Making Rational Decisions Using Adaptive Utility Elicitation.

Urszula Chajewska, Daphne Koller, Ronald Parr

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on on Innovative Applications of Artificial Intelligence, July 30, 2000

1999

Reinforcement Learning Using Approximate Belief States.

Andres C. Rodriguez, Ronald Parr, Daphne Koller

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

Policy Search via Density Estimation.

Andrew Y. Ng, Ronald Parr, Daphne Koller

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

Computing Factored Value Functions for Policies in Structured MDPs.

Daphne Koller, Ronald Parr

Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999

1998

Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems.

Ronald Parr

Proceedings of the UAI '98: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998

1997

Reinforcement Learning with Hierarchies of Machines.

Ronald Parr, Stuart Russell

Proceedings of the Advances in Neural Information Processing Systems 10, 1997

Generalized Prioritized Sweeping.

David Andre, Nir Friedman, Ronald Parr

Proceedings of the Advances in Neural Information Processing Systems 10, 1997

1995

Approximating Optimal Policies for Partially Observable Stochastic Domains.

Ronald Parr, Stuart Russell

Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, 1995

1993

Provably Bounded Optimal Agents.

Stuart J. Russell, Devika Subramanian, Ronald Parr

Proceedings of the 13th International Joint Conference on Artificial Intelligence. Chambéry, France, August 28, 1993