McGovern, Amy - School of Computer Science, University of Oklahoma

• To appear in the 2001 International Conference on Machine Learning 1 Automatic Discovery of Subgoals in Reinforcement Learning
• Hierarchical Optimal Control of MDPs Amy McGovern
• J2.3 USING SPATIOTEMPORAL RELATIONAL DATA MINING TO IDENTIFY THE KEY PARAMETERS FOR ANTICIPATING ROTATION INITIATION IN
• University of Massachusetts, Amherst Technical Report Number 98-70 1 Macro-Actions in Reinforcement Learning: An Empirical
• Policy Gradient vs. Value Function Approximation: A Reinforcement Learning Shootout
• 4.3A Anticipating the formation of tornadoes through data mining
• Mobile Agents on the Digital Battlefield Martin O. Hofmann
• MULTI-MODAL UTILE DISTINCTIONS Multi-Modal Utile Distinctions
• Timothy Sliwinski Jon Trueblood
• International Workshop on Visual Analytics (2011) S. Miksch and G. Santucci (Editors)
• Data Min Knowl Disc (2011) 22:232258 DOI 10.1007/s10618-010-0193-7
• UNDERSTANDING SEVERE WEATHER PROCESSES THROUGH SPATIOTEMPORAL RELATIONAL RANDOM FORESTS
• Spatiotemporal Relational Probability Trees: An Introduction Amy McGovern
• Exploiting Relational Structure to Understand Publication Patterns in High-Energy Physics
• Accelerating Reinforcement Learning through the Discovery of Useful Subgoals Amy McGovern (amy@cs.umass.edu) and Andrew G. Barto (barto@cs.umass.edu)
• Open problem: Dynamic Relational Models for Improved Hazardous Weather Prediction
• Basic-block Instruction Scheduling Using Reinforcement Learning and Amy McGovern, Eliot Moss, and Andrew G. Barto
• 2.5 ANALYZING THE EFFECTS OF LOW LEVEL BOUNDARIES ON TORNADOGENESIS THROUGH SPATIOTEMPORAL
• McGovern, A. and Jensen, D. (2003) University of Massachusetts Amherst Technical Report Number 03-14 1 Chi-squared
• Spatiotemporal Relational Random Forests Timothy A. Supinie
• The Thing We Tried That Worked: Utile Distinctions for Relational Reinforcement Learning
• Scheduling StraightLine Code Using Reinforcement Learning and Rollouts
• Data Mining and Knowledge Discovery manuscript No. (will be inserted by the editor)
• Accelerating Reinforcement Learning through the Discovery of Useful Subgoals Amy McGovern (amy@cs.umass.edu) and Andrew G. Barto (barto@cs.umass.edu)
• Scheduling Straight-Line Code Using Reinforcement Learning and Rollouts
• Mobile Agents on the Digital Battlefield Martin O. Hofmann
• Kernels for the Investigation of Localized Spatiotemporal Transitions of Drought with Support Vector Machines
• Creating Significant Learning Experiences in Introductory Artificial Intelligence
• Utile Distinctions for Relational Reinforcement Learning William Dabney and Amy McGovern
• McGovern, A. and Jensen, D. (2003) University of Massachusetts Amherst Technical Report Number 0314 1 Chisquared
• Advances in Neural Information Processing Systems 11 Scheduling Straight-Line Code Using
• USING SPATIOTEMPORAL RELATIONAL RANDOM FORESTS TO IMPROVE OUR UNDERSTANDING OF SEVERE WEATHER PROCESSES
• CISE/IIS Highlights Template FY2009 Request for NSF Highlight
• Spatiotemporal relational random forest (SRRF)
• DOI 10.1007/s10994-011-5249-4 Machine learning in space: extending our reach
• Teaching Introductory Artificial Intelligence through Java-based Games Amy McGovern
• Expert Move Prediction for Computer Go using Spatial Probability Trees
• Supporting Transparent Thread Assignment in Heterogeneous Multicore Processors Using