World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
42
Citations
10209
World Ranking
6395
National Ranking
1756

Research.com Recognitions

  • 1973 - Member of the National Academy of Sciences

Overview

Ronald Parr is affiliated with Duke University in the United States. Their research primarily focuses on computer science, with specific contributions to fields such as artificial intelligence, management science and operations research, civil and structural engineering, economics and econometrics, and statistical and nonlinear physics.

Their scholarly output includes eleven publications in artificial intelligence and related areas. Key topics covered in their work include:

  • Reinforcement Learning in Robotics
  • Imbalanced Data Classification Techniques
  • Machine Learning and Data Classification
  • Machine Learning and Algorithms
  • Game Theory and Applications
  • Infrastructure Resilience and Vulnerability Analysis
  • Game Theory and Voting Systems

Ronald Parr has collaborated frequently with several researchers, including Lesia Semenova, Cynthia Rudin, George Konidaris, Michael L. Littman, and Kavosh Asadi.

Their recent papers include:

  • On the Existence of Simpler Machine Learning Models, 2022, 2022 ACM Conference on Fairness, Accountability, and Transparency
  • Computing Optimal Strategies to Commit to in Stochastic Games, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Deep Radial-Basis Value Functions for Continuous Control, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Fitted Q-Learning for Relational Domains, 2020, arXiv (Cornell University)
  • Deep Radial-Basis Value Functions for Continuous Control, 2020, arXiv (Cornell University)

The main venues where their work has appeared are arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence, and the 2022 ACM Conference on Fairness, Accountability, and Transparency. They have also contributed to PubMed.

Ronald Parr has been recognized as a member of the National Academy of Sciences since 1973.

Best Publications

  • Least-squares policy iteration

    Michail G. Lagoudakis;Ronald Parr

  • Reinforcement Learning with Hierarchies of Machines

    Ronald Parr;Stuart J. Russell

  • Efficient solution algorithms for factored MDPs

    Carlos Guestrin;Daphne Koller;Ronald Parr;Shobha Venkataraman

  • Multiagent Planning with Factored MDPs

    Carlos Guestrin;Daphne Koller;Ronald Parr

  • DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks

    Austin Eliazar;Ronald Parr

  • Coordinated Reinforcement Learning

    Carlos Guestrin;Michail G. Lagoudakis;Ronald Parr

  • Making Rational Decisions Using Adaptive Utility Elicitation

    Urszula Chajewska;Daphne Koller;Ronald Parr

  • Hierarchical control and learning for markov decision processes

    Ronald Edward Parr;Stuart Russell

  • Bayesian Fault Detection and Diagnosis in Dynamic Systems

    Uri Lerner;Ronald Parr;Daphne Koller;Gautam Biswas

  • DP-SLAM 2.0

    A.I. Eliazar;R. Parr

  • Approximating optimal policies for partially observable stochastic domains

    Ronald Parr;Stuart Russell

  • Policy Iteration for Factored MDPs

    Daphne Koller;Ronald Parr

  • An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning

    Ronald Parr;Lihong Li;Gavin Taylor;Christopher Painter-Wakefield

  • Complexity of computing optimal stackelberg strategies in security resource allocation games

    Dmytro Korzhyk;Vincent Conitzer;Ronald Parr

  • Reinforcement learning as classification: leveraging modern classifiers

    Michail G. Lagoudakis;Ronald Parr

  • Computing Factored Value Functions for Policies in Structured MDPs

    Daphne Koller;Ronald Parr

  • Analyzing feature generation for value-function approximation

    Ronald Parr;Christopher Painter-Wakefield;Lihong Li;Michael Littman

  • Max-norm projections for factored MDPs

    Carlos Guestrin;Daphne Koller;Ronald Parr

  • Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms

    Uri Lerner;Ronald Parr

  • Kernelized value function approximation for reinforcement learning

    Gavin Taylor;Ronald Parr

Frequent Co-Authors

Daphne Koller
Daphne Koller insitro Inc.
Vincent Conitzer
Vincent Conitzer Carnegie Mellon University
Lawrence Carin
Lawrence Carin Duke University
Michael L. Littman
Michael L. Littman Brown University
Carlos Guestrin
Carlos Guestrin Stanford University
Lihong Li
Lihong Li Amazon (United States)
Cynthia Rudin
Cynthia Rudin Duke University
George Konidaris
George Konidaris Brown University
Shlomo Zilberstein
Shlomo Zilberstein University of Massachusetts Amherst
Min Wang
Min Wang Google (United States)

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