Ronald Parr - RankMe

80 papers A* 34A 9C 1Misc 1Journal 21Unranked 14

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Unifying View of Linear Function Approximation in Off-Policy RL Through Matrix Splitting and Preconditioning. CoRR Zechen Wu, Amy Greenwald, Ronald Parr
2024	J	`jnl`	Amazing Things Come From Having Many Good Models. CoRR Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner
2024	J	`jnl`	An Optimal Tightness Bound for the Simulation Lemma. RLJ Sam Lobel, Ronald Parr
2024	J	`jnl`	An Optimal Tightness Bound for the Simulation Lemma. CoRR Sam Lobel, Ronald Parr
2024	A*	`conf`	Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy. NeurIPS Cameron Allen, Aaron Kirtland, Ruo Yu Tao, Sam Lobel, Daniel Scott, Nicholas Petrocelli, Omer Gottesman, Ronald Parr, Michael L. Littman, George Konidaris
2024	J	`jnl`	Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy. CoRR Cameron Allen, Aaron Kirtland, Ruo Yu Tao, Sam Lobel, Daniel Scott, Nicholas Petrocelli, Omer Gottesman, Ronald Parr, Michael L. Littman, George Konidaris
2024	A*	`conf`	Position: Amazing Things Come From Having Many Good Models. ICML Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner
2024	A*	`conf`	Using Noise to Infer Aspects of Simplicity Without Learning. NeurIPS Zachery Boner, Harry Chen, Lesia Semenova, Ronald Parr, Cynthia Rudin
2023	A*	`conf`	A Path to Simpler Models Starts With Noise. NeurIPS Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin
2023	J	`jnl`	A Path to Simpler Models Starts With Noise. CoRR Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin
2022	—	`conf`	On the Existence of Simpler Machine Learning Models. FAccT Lesia Semenova, Cynthia Rudin, Ronald Parr
2020	J	`jnl`	Fitted Q-Learning for Relational Domains. CoRR Srijita Das, Sriraam Natarajan, Kaushik Roy, Ronald Parr, Kristian Kersting
2019	A*	`conf`	Revisiting the Softmax Bellman Operator: New Benefits and New Perspective. ICML Zhao Song, Ronald Parr, Lawrence Carin
2016	A*	`conf`	Distance Minimization for Reward Learning from Scored Trajectories. AAAI Benjamin Burchfiel, Carlo Tomasi, Ronald Parr
2016	A*	`conf`	Efficient PAC-Optimal Exploration in Concurrent, Continuous State MDPs with Delayed Updates. AAAI Jason Pazis, Ronald Parr
2016	—	`conf`	Improving PAC Exploration Using the Median Of Means. NIPS Jason Pazis, Ronald Parr, Jonathan P. How
2014	A*	`conf`	Unsupervised discovery of object classes with a mobile robot. ICRA Julian Mason, Bhaskara Marthi, Ronald Parr
2013	J	`jnl`	Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems CoRR Ronald Parr
2013	J	`jnl`	Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms CoRR Uri Lerner, Ronald Parr
2013	A*	`conf`	PAC Optimal Exploration in Continuous Space Markov Decision Processes. AAAI Jason Pazis, Ronald Parr
2013	J	`jnl`	Policy Iteration for Factored MDPs CoRR Daphne Koller, Ronald Parr
2013	A*	`conf`	Sample Complexity and Performance Bounds for Non-Parametric Approximate Linear Programming. AAAI Jason Pazis, Ronald Parr
2013	J	`jnl`	Value Function Approximation in Zero-Sum Markov Games CoRR Michail G. Lagoudakis, Ronald Parr
2012	A*	`conf`	Computing Optimal Strategies to Commit to in Stochastic Games. AAAI Joshua Letchford, Liam MacDermed, Vincent Conitzer, Ronald Parr, Charles L. Isbell Jr.
2012	J	`jnl`	Computing Stackelberg strategies in stochastic games. SIGecom Exch. Joshua Letchford, Liam MacDermed, Vincent Conitzer, Ronald Parr, Charles L. Isbell Jr.
2012	J	`jnl`	Efficient Selection of Disambiguating Actions for Stereo Vision. CoRR Monika Schaeffer, Ronald Parr
2012	A*	`conf`	Greedy Algorithms for Sparse Reinforcement Learning. ICML Christopher Painter-Wakefield, Ronald Parr
2012	A	`conf`	Object disappearance for object discovery. IROS Julian Mason, Bhaskara Marthi, Ronald Parr
2012	J	`jnl`	Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (2007) CoRR Ronald Parr, Linda C. van der Gaag
2012	J	`jnl`	Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs CoRR Gavin Taylor, Ronald Parr
2012	A	`conf`	Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs. UAI Gavin Taylor, Ronald Parr
2011	J	`jnl`	Efficient Solution Algorithms for Factored MDPs CoRR Carlos Guestrin, Daphne Koller, Ronald Parr, Shobha Venkataraman
2011	A*	`conf`	Generalized Value Functions for Large Action Sets. ICML Jason Pazis, Ronald Parr
2011	A*	`conf`	Non-Parametric Approximate Linear Programming for MDPs. AAAI Jason Pazis, Ronald Parr
2011	A*	`conf`	Security Games with Multiple Attacker Resources. IJCAI Dmytro Korzhyk, Vincent Conitzer, Ronald Parr
2011	A	`conf`	Solving Stackelberg games with uncertain observability. AAMAS Dmytro Korzhyk, Vincent Conitzer, Ronald Parr
2011	A*	`conf`	Textured occupancy grids for monocular localization without features. ICRA Julian Mason, Susanna Ricco, Ronald Parr
2010	A*	`conf`	Complexity of Computing Optimal Stackelberg Strategies in Security Resource Allocation Games. AAAI Dmytro Korzhyk, Vincent Conitzer, Ronald Parr
2010	J	`jnl`	Counting Objects with a Combination of Horizontal and Overhead Sensors. Int. J. Robotics Res. Erik Halvorson, Ronald Parr
2010	J	`jnl`	Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes CoRR Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein
2010	A*	`conf`	Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes. ICML Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein
2010	—	`conf`	Linear Complementarity for Regularized Policy Evaluation and Improvement. NIPS Jeffrey Johns, Christopher Painter-Wakefield, Ronald Parr
2009	A*	`conf`	Kernelized value function approximation for reinforcement learning. ICML Gavin Taylor, Ronald Parr
2009	A*	`conf`	Multi-Step Multi-Sensor Hider-Seeker Games. IJCAI Erik Halvorson, Vincent Conitzer, Ronald Parr
2008	A*	`conf`	An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning. ICML Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield, Michael L. Littman
2008	Misc	`conf`	Planning Aims for a Network of Horizontal and Overhead Sensors. ISAIM Erik Halvorson, Ronald Parr
2008	C	`conf`	Planning Aims for a Network of Horizontal and Overhead Sensors. WAFR Erik Halvorson, Ronald Parr
2007	A*	`conf`	Analyzing feature generation for value-function approximation. ICML Ronald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman
2007	J	`jnl`	Nonmyopic Multiaspect Sensing With Partially Observable Markov Decision Processes. IEEE Trans. Signal Process. Shihao Ji, Ronald Parr, Lawrence Carin
2007	A*	`conf`	Point-Based Policy Iteration. AAAI Shihao Ji, Ronald Parr, Hui Li, Xuejun Liao, Lawrence Carin
2007	A	`ed.`	UAI 2007, Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence, Vancouver, BC, Canada, July 19-22, 2007 UAI Ronald Parr, Linda C. van der Gaag
2006	A	`conf`	Efficient Selection of Disambiguating Actions for Stereo Vision. UAI Monika Schaeffer, Ronald Parr
2005	—	`conf`	Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps. NIPS Austin I. Eliazar, Ronald Parr
2004	A*	`conf`	DP-SLAM 2.0. ICRA Austin I. Eliazar, Ronald Parr
2004	A*	`conf`	Learning probabilistic motion models for mobile robots. ICML Austin I. Eliazar, Ronald Parr
2003	A*	`conf`	Approximate Policy Iteration using Large-Margin Classifiers. IJCAI Michail G. Lagoudakis, Ronald Parr
2003	A*	`conf`	DP-SLAM: Fast, Robust Simultaneous Localization and Mapping Without Predetermined Landmarks. IJCAI Austin I. Eliazar, Ronald Parr
2003	J	`jnl`	Efficient Solution Algorithms for Factored MDPs. J. Artif. Intell. Res. Carlos Guestrin, Daphne Koller, Ronald Parr, Shobha Venkataraman
2003	J	`jnl`	Least-Squares Policy Iteration. J. Mach. Learn. Res. Michail G. Lagoudakis, Ronald Parr
2003	A*	`conf`	Reinforcement Learning as Classification: Leveraging Modern Classifiers. ICML Michail G. Lagoudakis, Ronald Parr
2002	A*	`conf`	Coordinated Reinforcement Learning. ICML Carlos Guestrin, Michail G. Lagoudakis, Ronald Parr
2002	—	`conf`	Learning in Zero-Sum Team Markov Games Using Factored Value Functions. NIPS Michail G. Lagoudakis, Ronald Parr
2002	—	`conf`	Least-Squares Methods in Reinforcement Learning for Control. SETN Michail G. Lagoudakis, Ronald Parr, Michael L. Littman
2002	A	`conf`	Value Function Approximation in Zero-Sum Markov Games. UAI Michail G. Lagoudakis, Ronald Parr
2002	A*	`conf`	XPathLearner: An On-line Self-Tuning Markov Histogram for XML Path Selectivity Estimation. VLDB Lipyeow Lim, Min Wang, Sriram Padmanabhan, Jeffrey Scott Vitter, Ronald Parr
2001	A	`conf`	Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms. UAI Uri Lerner, Ronald Parr
2001	A*	`conf`	Max-norm Projections for Factored MDPs. IJCAI Carlos Guestrin, Daphne Koller, Ronald Parr
2001	—	`conf`	Model-Free Least-Squares Policy Iteration. NIPS Michail G. Lagoudakis, Ronald Parr
2001	—	`conf`	Multiagent Planning with Factored MDPs. NIPS Carlos Guestrin, Daphne Koller, Ronald Parr
2000	—	`conf`	Bayesian Fault Detection and Diagnosis in Dynamic Systems. AAAI/IAAI Uri Lerner, Ronald Parr, Daphne Koller, Gautam Biswas
2000	—	`conf`	Making Rational Decisions Using Adaptive Utility Elicitation. AAAI/IAAI Urszula Chajewska, Daphne Koller, Ronald Parr
2000	A	`conf`	Policy Iteration for Factored MDPs. UAI Daphne Koller, Ronald Parr
1999	A*	`conf`	Computing Factored Value Functions for Policies in Structured MDPs. IJCAI Daphne Koller, Ronald Parr
1999	—	`conf`	Policy Search via Density Estimation. NIPS Andrew Y. Ng, Ronald Parr, Daphne Koller
1999	—	`conf`	Reinforcement Learning Using Approximate Belief States. NIPS Andres C. Rodriguez, Ronald Parr, Daphne Koller
1998	A	`conf`	Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems. UAI Ronald Parr
1997	—	`conf`	Generalized Prioritized Sweeping. NIPS David Andre, Nir Friedman, Ronald Parr
1997	—	`conf`	Reinforcement Learning with Hierarchies of Machines. NIPS Ronald Parr, Stuart Russell
1995	A*	`conf`	Approximating Optimal Policies for Partially Observable Stochastic Domains. IJCAI Ronald Parr, Stuart Russell
1993	A*	`conf`	Provably Bounded Optimal Agents. IJCAI Stuart J. Russell, Devika Subramanian, Ronald Parr