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ORCIDRonald Parr
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- affiliation: Duke University, Durham, NC, USA
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2020 – today
- 2024
[j6]Sam Lobel, Ronald Parr:
An Optimal Tightness Bound for the Simulation Lemma. RLJ 2: 785-797 (2024)- [c58]Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner:
Position: Amazing Things Come From Having Many Good Models. ICML 2024 - [i16]Sam Lobel, Ronald Parr:
An Optimal Tightness Bound for the Simulation Lemma. CoRR abs/2406.16249 (2024) - [i15]Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner:
Amazing Things Come From Having Many Good Models. CoRR abs/2407.04846 (2024) - [i14]Cameron Allen, Aaron Kirtland, Ruo Yu Tao, Sam Lobel, Daniel Scott, Nicholas Petrocelli, Omer Gottesman, Ronald Parr, Michael L. Littman, George Konidaris:
Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy. CoRR abs/2407.07333 (2024) - 2023
- [c57]Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin:
A Path to Simpler Models Starts With Noise. NeurIPS 2023 - [i13]Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin:
A Path to Simpler Models Starts With Noise. CoRR abs/2310.19726 (2023) - 2022
- [c56]Lesia Semenova, Cynthia Rudin, Ronald Parr:
On the Existence of Simpler Machine Learning Models. FAccT 2022: 1827-1858 - 2021
- [c55]Kavosh Asadi, Neev Parikh, Ronald E. Parr, George Dimitri Konidaris, Michael L. Littman:
Deep Radial-Basis Value Functions for Continuous Control. AAAI 2021: 6696-6704 - 2020
- [i12]Kavosh Asadi, Ronald E. Parr, George Dimitri Konidaris, Michael L. Littman:
Deep RBF Value Functions for Continuous Control. CoRR abs/2002.01883 (2020) - [i11]Srijita Das, Sriraam Natarajan, Kaushik Roy, Ronald Parr, Kristian Kersting:
Fitted Q-Learning for Relational Domains. CoRR abs/2006.05595 (2020)
2010 – 2019
- 2019
- [c54]Zhao Song, Ronald Parr, Lawrence Carin:
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective. ICML 2019: 5916-5925 - 2018
- [i10]Zhao Song, Ronald E. Parr, Lawrence Carin:
Revisiting the Softmax Bellman Operator: Theoretical Properties and Practical Benefits. CoRR abs/1812.00456 (2018) - 2016
- [c53]Jason Pazis, Ronald Parr:
Efficient PAC-Optimal Exploration in Concurrent, Continuous State MDPs with Delayed Updates. AAAI 2016: 1977-1985 - [c52]Benjamin Burchfiel, Carlo Tomasi, Ronald Parr:
Distance Minimization for Reward Learning from Scored Trajectories. AAAI 2016: 3330-3336 - [c51]Jason Pazis, Ronald Parr, Jonathan P. How:
Improving PAC Exploration Using the Median Of Means. NIPS 2016: 3891-3899 - [c50]Zhao Song, Ronald E. Parr, Xuejun Liao, Lawrence Carin:
Linear Feature Encoding for Reinforcement Learning. NIPS 2016: 4224-4232 - 2014
- [c49]Julian Mason, Bhaskara Marthi, Ronald Parr:
Unsupervised discovery of object classes with a mobile robot. ICRA 2014: 3074-3081 - 2013
- [c48]Jason Pazis, Ronald Parr:
PAC Optimal Exploration in Continuous Space Markov Decision Processes. AAAI 2013: 774-781 - [c47]Jason Pazis, Ronald Parr:
Sample Complexity and Performance Bounds for Non-Parametric Approximate Linear Programming. AAAI 2013: 782-788 - [i9]Michail G. Lagoudakis, Ronald Parr:
Value Function Approximation in Zero-Sum Markov Games. CoRR abs/1301.0580 (2013) - [i8]Uri Lerner, Ronald Parr:
Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms. CoRR abs/1301.2288 (2013) - [i7]Daphne Koller, Ronald Parr:
Policy Iteration for Factored MDPs. CoRR abs/1301.3869 (2013) - [i6]Ronald Parr:
Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems. CoRR abs/1301.7405 (2013) - 2012
- [j5]Joshua Letchford, Liam MacDermed, Vincent Conitzer, Ronald Parr, Charles L. Isbell Jr.:
Computing Stackelberg strategies in stochastic games. SIGecom Exch. 11(2): 36-40 (2012) - [c46]Joshua Letchford, Liam MacDermed, Vincent Conitzer, Ronald Parr, Charles L. Isbell Jr.:
Computing Optimal Strategies to Commit to in Stochastic Games. AAAI 2012: 1380-1386 - [c45]Christopher Painter-Wakefield, Ronald Parr:
Greedy Algorithms for Sparse Reinforcement Learning. ICML 2012 - [c44]Julian Mason, Bhaskara Marthi, Ronald Parr:
Object disappearance for object discovery. IROS 2012: 2836-2843 - [c43]Gavin Taylor, Ronald Parr:
Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs. UAI 2012: 835-842 - [i5]Monika Schaeffer, Ronald Parr:
Efficient Selection of Disambiguating Actions for Stereo Vision. CoRR abs/1206.6878 (2012) - [i4]Ronald Parr, Linda C. van der Gaag:
Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (2007). CoRR abs/1208.5155 (2012) - [i3]Gavin Taylor, Ronald Parr:
Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs. CoRR abs/1210.4898 (2012) - 2011
- [c42]Jason Pazis, Ronald Parr:
Non-Parametric Approximate Linear Programming for MDPs. AAAI 2011: 459-464 - [c41]Dmytro Korzhyk, Vincent Conitzer, Ronald Parr:
Solving Stackelberg games with uncertain observability. AAMAS 2011: 1013-1020 - [c40]Jason Pazis, Ronald Parr:
Generalized Value Functions for Large Action Sets. ICML 2011: 1185-1192 - [c39]Julian Mason, Susanna Ricco, Ronald Parr:
Textured occupancy grids for monocular localization without features. ICRA 2011: 5800-5806 - [c38]Dmytro Korzhyk, Vincent Conitzer, Ronald Parr:
Security Games with Multiple Attacker Resources. IJCAI 2011: 273-279 - [i2]Carlos Guestrin, Daphne Koller, Ronald Parr, Shobha Venkataraman:
Efficient Solution Algorithms for Factored MDPs. CoRR abs/1106.1822 (2011) - 2010
- [j4]Erik Halvorson, Ronald Parr:
Counting Objects with a Combination of Horizontal and Overhead Sensors. Int. J. Robotics Res. 29(7): 840-854 (2010) - [c37]Dmytro Korzhyk, Vincent Conitzer, Ronald Parr:
Complexity of Computing Optimal Stackelberg Strategies in Security Resource Allocation Games. AAAI 2010: 805-810 - [c36]Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein:
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes. ICML 2010: 871-878 - [c35]Jeffrey Johns, Christopher Painter-Wakefield, Ronald Parr:
Linear Complementarity for Regularized Policy Evaluation and Improvement. NIPS 2010: 1009-1017 - [i1]Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein:
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes. CoRR abs/1005.1860 (2010)
2000 – 2009
- 2009
- [c34]Gavin Taylor, Ronald Parr:
Kernelized value function approximation for reinforcement learning. ICML 2009: 1017-1024 - [c33]Erik Halvorson, Vincent Conitzer, Ronald Parr:
Multi-Step Multi-Sensor Hider-Seeker Games. IJCAI 2009: 159-166 - 2008
- [c32]Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield, Michael L. Littman:
An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning. ICML 2008: 752-759 - [c31]Erik Halvorson, Ronald Parr:
Planning Aims for a Network of Horizontal and Overhead Sensors. ISAIM 2008 - [c30]Erik Halvorson, Ronald Parr:
Planning Aims for a Network of Horizontal and Overhead Sensors. WAFR 2008: 19-34 - 2007
- [j3]Shihao Ji
, Ronald Parr, Lawrence Carin:
Nonmyopic Multiaspect Sensing With Partially Observable Markov Decision Processes. IEEE Trans. Signal Process. 55(6-1): 2720-2730 (2007) - [c29]Shihao Ji, Ronald Parr, Hui Li, Xuejun Liao, Lawrence Carin:
Point-Based Policy Iteration. AAAI 2007: 1243-1249 - [c28]Ronald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman:
Analyzing feature generation for value-function approximation. ICML 2007: 737-744 - [e1]Ronald Parr, Linda C. van der Gaag:
UAI 2007, Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence, Vancouver, BC, Canada, July 19-22, 2007. AUAI Press 2007, ISBN 0-9749039-3-0 [contents] - 2006
- [c27]Monika Schaeffer, Ronald Parr:
Efficient Selection of Disambiguating Actions for Stereo Vision. UAI 2006 - 2005
- [c26]Austin I. Eliazar, Ronald Parr:
Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps. NIPS 2005: 339-346 - 2004
- [c25]Austin I. Eliazar, Ronald Parr:
Learning probabilistic motion models for mobile robots. ICML 2004 - [c24]Austin I. Eliazar, Ronald Parr:
DP-SLAM 2.0. ICRA 2004: 1314-1320 - 2003
- [j2]Carlos Guestrin, Daphne Koller, Ronald Parr, Shobha Venkataraman:
Efficient Solution Algorithms for Factored MDPs. J. Artif. Intell. Res. 19: 399-468 (2003) - [j1]Michail G. Lagoudakis, Ronald Parr:
Least-Squares Policy Iteration. J. Mach. Learn. Res. 4: 1107-1149 (2003) - [c23]Michail G. Lagoudakis, Ronald Parr:
Reinforcement Learning as Classification: Leveraging Modern Classifiers. ICML 2003: 424-431 - [c22]Austin I. Eliazar, Ronald Parr:
DP-SLAM: Fast, Robust Simultaneous Localization and Mapping Without Predetermined Landmarks. IJCAI 2003: 1135-1142 - [c21]Michail G. Lagoudakis, Ronald Parr:
Approximate Policy Iteration using Large-Margin Classifiers. IJCAI 2003: 1432-1434 - 2002
- [c20]Carlos Guestrin, Michail G. Lagoudakis, Ronald Parr:
Coordinated Reinforcement Learning. ICML 2002: 227-234 - [c19]Michail G. Lagoudakis, Ronald Parr:
Learning in Zero-Sum Team Markov Games Using Factored Value Functions. NIPS 2002: 1627-1634 - [c18]Michail G. Lagoudakis, Ronald Parr, Michael L. Littman:
Least-Squares Methods in Reinforcement Learning for Control. SETN 2002: 249-260 - [c17]Michail G. Lagoudakis, Ronald Parr:
Value Function Approximation in Zero-Sum Markov Games. UAI 2002: 283-292 - [c16]Lipyeow Lim, Min Wang, Sriram Padmanabhan, Jeffrey Scott Vitter, Ronald Parr:
XPathLearner: An On-line Self-Tuning Markov Histogram for XML Path Selectivity Estimation. VLDB 2002: 442-453 - 2001
- [c15]Carlos Guestrin, Daphne Koller, Ronald Parr:
Max-norm Projections for Factored MDPs. IJCAI 2001: 673-682 - [c14]Carlos Guestrin, Daphne Koller, Ronald Parr:
Multiagent Planning with Factored MDPs. NIPS 2001: 1523-1530 - [c13]Michail G. Lagoudakis, Ronald Parr:
Model-Free Least-Squares Policy Iteration. NIPS 2001: 1547-1554 - [c12]Uri Lerner, Ronald Parr:
Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms. UAI 2001: 310-318 - 2000
- [c11]Urszula Chajewska, Daphne Koller, Ronald Parr:
Making Rational Decisions Using Adaptive Utility Elicitation. AAAI/IAAI 2000: 363-369 - [c10]Uri Lerner, Ronald Parr, Daphne Koller, Gautam Biswas:
Bayesian Fault Detection and Diagnosis in Dynamic Systems. AAAI/IAAI 2000: 531-537 - [c9]Daphne Koller, Ronald Parr:
Policy Iteration for Factored MDPs. UAI 2000: 326-334
1990 – 1999
- 1999
- [c8]Daphne Koller, Ronald Parr:
Computing Factored Value Functions for Policies in Structured MDPs. IJCAI 1999: 1332-1339 - [c7]Andrew Y. Ng, Ronald Parr, Daphne Koller:
Policy Search via Density Estimation. NIPS 1999: 1022-1028 - [c6]Andres C. Rodriguez, Ronald Parr, Daphne Koller:
Reinforcement Learning Using Approximate Belief States. NIPS 1999: 1036-1042 - 1998
- [c5]Ronald Parr:
Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems. UAI 1998: 422-430 - 1997
- [c4]David Andre, Nir Friedman, Ronald Parr:
Generalized Prioritized Sweeping. NIPS 1997: 1001-1007 - [c3]Ronald Parr, Stuart Russell:
Reinforcement Learning with Hierarchies of Machines. NIPS 1997: 1043-1049 - 1995
- [c2]Ronald Parr, Stuart Russell:
Approximating Optimal Policies for Partially Observable Stochastic Domains. IJCAI 1995: 1088-1095 - 1993
- [c1]Stuart J. Russell, Devika Subramanian, Ronald Parr:
Provably Bounded Optimal Agents. IJCAI 1993: 338-345
Coauthor Index
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