Ronald Parr

	2009
35		Gavin Taylor, Ronald Parr: Kernelized value function approximation for reinforcement learning. ICML 2009: 128
34		Erik Halvorson, Vincent Conitzer, Ronald Parr: Multi-Step Multi-Sensor Hider-Seeker Games. IJCAI 2009: 159-166
	2008
33		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
	2007
32		Shihao Ji, Ronald Parr, Hui Li, Xuejun Liao, Lawrence Carin: Point-Based Policy Iteration. AAAI 2007: 1243-1249
31		Ronald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman: Analyzing feature generation for value-function approximation. ICML 2007: 737-744
30		Shihao Ji, Ronald Parr, Lawrence Carin: Nonmyopic Multiaspect Sensing With Partially Observable Markov Decision Processes. IEEE Transactions on Signal Processing 55(6-1): 2720-2730 (2007)
	2006
29		Monika Schaeffer, Ronald Parr: Efficient Selection of Disambiguating Actions for Stereo Vision. UAI 2006
	2005
28		Austin I. Eliazar, Ronald Parr: Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps. NIPS 2005
	2004
27		Austin I. Eliazar, Ronald Parr: Learning probabilistic motion models for mobile robots. ICML 2004
26		Austin I. Eliazar, Ronald Parr: DP-SLAM 2.0. ICRA 2004: 1314-1320
	2003
25		Michail G. Lagoudakis, Ronald Parr: Reinforcement Learning as Classification: Leveraging Modern Classifiers. ICML 2003: 424-431
24		Austin I. Eliazar, Ronald Parr: DP-SLAM: Fast, Robust Simultaneous Localization and Mapping Without Predetermined Landmarks. IJCAI 2003: 1135-1142
23		Michail G. Lagoudakis, Ronald Parr: Approximate Policy Iteration using Large-Margin Classifiers. IJCAI 2003: 1432-1434
22		Carlos Guestrin, Daphne Koller, Ronald Parr, Shobha Venkataraman: Efficient Solution Algorithms for Factored MDPs. J. Artif. Intell. Res. (JAIR) 19: 399-468 (2003)
21		Michail G. Lagoudakis, Ronald Parr: Least-Squares Policy Iteration. Journal of Machine Learning Research 4: 1107-1149 (2003)
	2002
20		Carlos Guestrin, Michail G. Lagoudakis, Ronald Parr: Coordinated Reinforcement Learning. ICML 2002: 227-234
19		Michail G. Lagoudakis, Ronald Parr: Learning in Zero-Sum Team Markov Games Using Factored Value Functions. NIPS 2002: 1627-1634
18		Michail G. Lagoudakis, Ronald Parr, Michael L. Littman: Least-Squares Methods in Reinforcement Learning for Control. SETN 2002: 249-260
17		Michail G. Lagoudakis, Ronald Parr: Value Function Approximation in Zero-Sum Markov Games. UAI 2002: 283-292
16		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
15		Carlos Guestrin, Daphne Koller, Ronald Parr: Max-norm Projections for Factored MDPs. IJCAI 2001: 673-682
14		Carlos Guestrin, Daphne Koller, Ronald Parr: Multiagent Planning with Factored MDPs. NIPS 2001: 1523-1530
13		Michail G. Lagoudakis, Ronald Parr: Model-Free Least-Squares Policy Iteration. NIPS 2001: 1547-1554
12		Uri Lerner, Ronald Parr: Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms. UAI 2001: 310-318
	2000
11		Urszula Chajewska, Daphne Koller, Ronald Parr: Making Rational Decisions Using Adaptive Utility Elicitation. AAAI/IAAI 2000: 363-369
10		Uri Lerner, Ronald Parr, Daphne Koller, Gautam Biswas: Bayesian Fault Detection and Diagnosis in Dynamic Systems. AAAI/IAAI 2000: 531-537
9		Daphne Koller, Ronald Parr: Policy Iteration for Factored MDPs. UAI 2000: 326-334
	1999
8		Daphne Koller, Ronald Parr: Computing Factored Value Functions for Policies in Structured MDPs. IJCAI 1999: 1332-1339
7		Andrew Y. Ng, Ronald Parr, Daphne Koller: Policy Search via Density Estimation. NIPS 1999: 1022-1028
6		Andrés Rodríguez, Ronald Parr, Daphne Koller: Reinforcement Learning Using Approximate Belief States. NIPS 1999: 1036-1042
	1998
5		Ronald Parr: Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems. UAI 1998: 422-430
	1997
4		David Andre, Nir Friedman, Ronald Parr: Generalized Prioritized Sweeping. NIPS 1997
3		Ronald Parr, Stuart J. Russell: Reinforcement Learning with Hierarchies of Machines. NIPS 1997
	1995
2		Ronald Parr, Stuart J. Russell: Approximating Optimal Policies for Partially Observable Stochastic Domains. IJCAI 1995: 1088-1095
	1993
1		Stuart J. Russell, Devika Subramanian, Ronald Parr: Provably Bounded Optimal Agents. IJCAI 1993: 338-345

Coauthor Index

1	David Andre	[4]
2	Gautam Biswas	[10]
3	Lawrence Carin	[30] [32]
4	Urszula Chajewska	[11]
5	Vincent Conitzer	[34]
6	Austin I. Eliazar	[24] [26] [27] [28]
7	Nir Friedman	[4]
8	Carlos Guestrin	[14] [15] [20] [22]
9	Erik Halvorson	[34]
10	Shihao Ji	[30] [32]
11	Daphne Koller	[6] [7] [8] [9] [10] [11] [14] [15] [22]
12	Michail G. Lagoudakis	[13] [17] [18] [19] [20] [21] [23] [25]
13	Uri Lerner	[10] [12]
14	Hui Li	[32]
15	Lihong Li	[31] [33]
16	Xuejun Liao	[32]
17	Lipyeow Lim	[16]
18	Michael L. Littman	[18] [31] [33]
19	Andrew Y. Ng	[7]
20	Sriram Padmanabhan	[16]
21	Christopher Painter-Wakefield	[31] [33]
22	Andrés Rodríguez	[6]
23	Stuart J. Russell	[1] [2] [3]
24	Monika Schaeffer	[29]
25	Devika Subramanian	[1]
26	Gavin Taylor	[33] [35]
27	Shobha Venkataraman	[22]
28	Jeffrey Scott Vitter	[16]
29	Min Wang	[16]

Colors in the list of coauthors