Dietterich, Thomas G.

107 publications

TMLR 2023 Hidden Heterogeneity: When to Choose Similarity-Based Calibration Kiri L. Wagstaff, Thomas G Dietterich

JMLR 2022 PAC Guarantees and Effective Algorithms for Detecting Novel Categories Si Liu, Risheek Garrepalli, Dan Hendrycks, Alan Fern, Debashis Mondal, Thomas G. Dietterich

ICCV 2021 Confidence Calibration for Domain Generalization Under Covariate Shift Yunye Gong, Xiao Lin, Yi Yao, Thomas G. Dietterich, Ajay Divakaran, Melinda Gervasio

AAAI 2021 K-N-MOMDPs: Towards Interpretable Solutions for Adaptive Management Jonathan Ferrer-Mestres, Thomas G. Dietterich, Olivier Buffet, Iadine Chades

IJCAI 2019 Three-Quarter Sibling Regression for Denoising Observational Data Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich

JAIR 2017 Sample-Based Tree Search with Fixed and Adaptive State Abstractions Jesse Hostetler, Alan Fern, Thomas G. Dietterich

UAI 2016 Finite Sample Complexity of Rare Pattern Anomaly Detection Md Amran Siddiqui, Alan Fern, Thomas G. Dietterich, Shubhomoy Das

IJCAI 2016 Transductive Optimization of Top K Precision Li-Ping Liu, Thomas G. Dietterich, Nan Li, Zhi-Hua Zhou

CVPR 2015 HC-Search for Structured Prediction in Computer Vision Michael Lam, Janardhan Rao Doppa, Sinisa Todorovic, Thomas G. Dietterich

AAAI 2015 Learning Greedy Policies for the Easy-First Framework Jun Xie, Chao Ma, Janardhan Rao Doppa, Prashanth Mannem, Xiaoli Z. Fern, Thomas G. Dietterich, Prasad Tadepalli

JMLR 2015 PAC Optimal MDP Planning with Application to Invasive Species Management Majid Alkaee Taleghan, Thomas G. Dietterich, Mark Crowley, Kim Hall, H. Jo Albers

UAI 2015 Progressive Abstraction Refinement for Sparse Sampling Jesse Hostetler, Alan Fern, Thomas G. Dietterich

JMLR 2014 Active Imitation Learning: Formal and Practical Reductions to I.I.D. Learning Kshitij Judah, Alan P. Fern, Thomas G. Dietterich, Prasad Tadepalli

AAAI 2014 Learning Scripts as Hidden Markov Models John Walker Orr, Prasad Tadepalli, Janardhan Rao Doppa, Xiaoli Z. Fern, Thomas G. Dietterich

AAAI 2013 Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar Daniel Sheldon, Andrew Farnsworth, Jed Irvine, Benjamin Van Doren, Kevin F. Webb, Thomas G. Dietterich, Steve Kelling

AAAI 2013 Guiding Scientific Discovery with Explanations Using DEMUD Kiri L. Wagstaff, Nina L. Lanza, David R. Thompson, Thomas G. Dietterich, Martha S. Gilmore

ICCVW 2013 Learning to Detect Basal Tubules of Nematocysts in SEM Images Michael Lam, Janardhan Rao Doppa, Xu Hu, Sinisa Todorovic, Thomas G. Dietterich, Abigail Reft, Marymegan Daly

AAAI 2013 PAC Optimal Planning for Invasive Species Management: Improved Exploration for Reinforcement Learning from Simulator-Defined MDPs Thomas G. Dietterich, Majid Alkaee Taleghan, Mark Crowley

ICCVW 2013 Zero-Shot Learning and Detection of Teeth in Images of Bat Skulls Xu Hu, Michael Lam, Sinisa Todorovic, Thomas G. Dietterich, Maureen A. OLeary, Andrea L. Cirranello, Nancy B. Simmons, Paúl M. Velazco

NeurIPS 2012 A Conditional Multinomial Mixture Model for Superset Label Learning Liping Liu, Thomas G. Dietterich

UAI 2012 Active Imitation Learning via Reduction to I.I.D. Active Learning Kshitij Judah, Alan Fern, Thomas G. Dietterich

UAI 2012 Inferring Strategies from Limited Reconnaissance in Real-Time Strategy Games Jesse Hostetler, Ethan W. Dereszynski, Thomas G. Dietterich, Alan Fern

NeurIPS 2011 Collective Graphical Models Daniel R. Sheldon, Thomas G. Dietterich

AAAI 2011 Incorporating Boosted Regression Trees into Ecological Latent Variable Models Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich

NeurIPS 2011 Inverting Grice's Maxims to Learn Rules from Natural Language Extractions Mohammad S. Sorower, Janardhan R. Doppa, Walker Orr, Prasad Tadepalli, Thomas G. Dietterich, Xiaoli Z. Fern

ACML 2011 Learning Rules from Incomplete Examples via Implicit Mention Models Janardhan Rao Doppa, Mohammad Shahed Sorower, Mohammad Nasresfahani, Jed Irvine, Walker Orr, Thomas G. Dietterich, Xiaoli Fern, Prasad Tadepalli

AAAI 2010 Reinforcement Learning via Practice and Critique Advice Kshitij Judah, Saikat Roy, Alan Fern, Thomas G. Dietterich

CVPR 2009 Dictionary-Free Categorization of Very Similar Objects via Stacked Evidence Trees Gonzalo Martínez-Muñoz, Natalia Larios Delgado, Eric N. Mortensen, Wei Zhang, Asako Yamamuro, Robert Paasch, Nadia Payet, David A. Lytle, Linda G. Shapiro, Sinisa Todorovic, Andrew Moldenke, Thomas G. Dietterich

ICML 2009 Learning Non-Redundant Codebooks for Classifying Complex Objects Wei Zhang, Akshat Surve, Xiaoli Z. Fern, Thomas G. Dietterich

IJCAI 2009 Machine Learning in Ecosystem Informatics and Sustainability Thomas G. Dietterich

ICML 2008 Automatic Discovery and Transfer of MAXQ Hierarchies Neville Mehta, Soumya Ray, Prasad Tadepalli, Thomas G. Dietterich

JMLR 2008 Gradient Tree Boosting for Training Conditional Random Fields Thomas G. Dietterich, Guohua Hao, Adam Ashenfelter

AAAI 2008 Integrating Multiple Learning Components Through Markov Logic Thomas G. Dietterich, Xinlong Bao

ECML-PKDD 2008 Learning MDP Action Models via Discrete Mixture Trees Michael Wynkoop, Thomas G. Dietterich

MLJ 2008 Structured Machine Learning: The Next Ten Years Thomas G. Dietterich, Pedro M. Domingos, Lise Getoor, Stephen H. Muggleton, Prasad Tadepalli

ALT 2007 Machine Learning in Ecosystem Informatics Thomas G. Dietterich

CVPR 2007 Principal Curvature-Based Region Detector for Object Recognition Hongli Deng, Wei Zhang, Eric N. Mortensen, Thomas G. Dietterich, Linda G. Shapiro

UAI 2007 Probabilistic Models for Anomaly Detection in Remote Sensor Data Streams Ethan W. Dereszynski, Thomas G. Dietterich

IJCAI 2007 Real-Time Detection of Task Switches of Desktop Users Jianqiang Shen, Lida Li, Thomas G. Dietterich

CVPRW 2006 Reinforcement Matching Using Region Context Hongli Deng, Eric N. Mortensen, Linda G. Shapiro, Thomas G. Dietterich

JAIR 2005 Integrating Learning from Examples into the Search for Diagnostic Policies Valentina Bayer Zubek, Thomas G. Dietterich

ICML 2005 Learning First-Order Probabilistic Models with Combining Rules Sriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo C. Restificar

UAI 2005 Learning from Sparse Data by Exploiting Monotonicity Constraints Eric Altendorf, Angelo C. Restificar, Thomas G. Dietterich

AAAI 2005 The TaskTracker System Simone Stumpf, Xinlong Bao, Anton N. Dragunov, Thomas G. Dietterich, Jonathan L. Herlocker, Kevin Johnsrude, Lida Li, Jianqiang Shen

JMLR 2004 Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods Giorgio Valentini, Thomas G. Dietterich

ICML 2004 Improving SVM Accuracy by Training on Auxiliary Data Sources Pengcheng Wu, Thomas G. Dietterich

ICML 2004 Training Conditional Random Fields via Gradient Tree Boosting Thomas G. Dietterich, Adam Ashenfelter, Yaroslav Bulatov

ICML 2003 Low Bias Bagged Support Vector Machines Giorgio Valentini, Thomas G. Dietterich

ICML 2003 Model-Based Policy Gradient Reinforcement Learning Xin Wang, Thomas G. Dietterich

ICML 2002 Action Refinement in Reinforcement Learning by Probability Smoothing Thomas G. Dietterich, Dídac Busquets, Ramón López de Mántaras, Carles Sierra

ICML 2002 Pruning Improves Heuristic Search for Cost-Sensitive Learning Valentina Bayer Zubek, Thomas G. Dietterich

NeurIPS 2001 Batch Value Function Approximation via Support Vectors Thomas G. Dietterich, Xin Wang

NeurIPS 2001 Stabilizing Value Function Approximation with the BFBP Algorithm Xin Wang, Thomas G. Dietterich

ECML-PKDD 2001 Support Vectors for Reinforcement Learning Thomas G. Dietterich, Xin Wang

ICML 2000 A Divide and Conquer Approach to Learning from Prior Knowledge Eric Chown, Thomas G. Dietterich

MLJ 2000 An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization Thomas G. Dietterich

ICML 2000 Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers Dragos D. Margineantu, Thomas G. Dietterich

JAIR 2000 Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition Thomas G. Dietterich

ALT 2000 The Divide-and-Conquer Manifesto Thomas G. Dietterich

NeurIPS 1999 State Abstraction in MAXQ Hierarchical Reinforcement Learning Thomas G. Dietterich

NeCo 1998 Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms Thomas G. Dietterich

ICML 1998 The MAXQ Method for Hierarchical Reinforcement Learning Thomas G. Dietterich

MLJ 1997 Explanation-Based Learning and Reinforcement Learning: A Unified View Thomas G. Dietterich, Nicholas S. Flann

ICML 1997 Hierarchical Explanation-Based Reinforcement Learning Prasad Tadepalli, Thomas G. Dietterich

ICML 1997 Pruning Adaptive Boosting Dragos D. Margineantu, Thomas G. Dietterich

ICML 1996 Applying the Waek Learning Framework to Understand and Improve C4.5 Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour

MLJ 1996 Editorial Thomas G. Dietterich

MLJ 1995 A Comparison of ID3 and Backpropagation for English Text-to-Speech Mapping Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri

IJCAI 1995 A Reinforcement Learning Approach to Job-Shop Scheduling Wei Zhang, Thomas G. Dietterich

MLJ 1995 An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms Dietrich Wettschereck, Thomas G. Dietterich

ICML 1995 Error-Correcting Output Coding Corrects Bias and Variance Eun Bae Kong, Thomas G. Dietterich

ICML 1995 Explanation-Based Learning and Reinforcement Learning: A Unified View Thomas G. Dietterich, Nicholas S. Flann

NeurIPS 1995 High-Performance Job-Shop Scheduling with a Time-Delay TD(λ) Network Wei Zhang, Thomas G. Dietterich

JAIR 1995 Solving Multiclass Learning Problems via Error-Correcting Output Codes Thomas G. Dietterich, Ghulum Bakiri

MLJ 1994 Editorial: New Editorial Board Members Thomas G. Dietterich

NeurIPS 1993 A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction Thomas G. Dietterich, Ajay N. Jain, Richard H. Lathrop, Tomás Lozano-Pérez

MLJ 1993 Editorial Thomas G. Dietterich

NeurIPS 1993 Locally Adaptive Nearest Neighbor Algorithms Dietrich Wettschereck, Thomas G. Dietterich

NeurIPS 1993 Memory-Based Methods for Regression and Classification Thomas G. Dietterich, Dietrich Wettschereck, Chris G. Atkeson, Andrew W. Moore

MLJ 1992 Editorial Thomas G. Dietterich

ICML 1992 On Learning More Concepts Hussein Almuallim, Thomas G. Dietterich

AAAI 1991 Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs Thomas G. Dietterich, Ghulum Bakiri

ICML 1991 Knowledge Compilation to Speed up Numerical Optimization Giuseppe Cerbone, Thomas G. Dietterich

AAAI 1991 Learning with Many Irrelevant Features Hussein Almuallim, Thomas G. Dietterich

ICML 1991 Machine Learning in Engineering Automation Steve A. Chien, Bradley L. Whitehall, Thomas G. Dietterich, Richard J. Doyle, Brian Falkenhainer, James Garrett, Stephen C. Y. Lu

ICML 1990 A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri

MLJ 1990 Exploratory Research in Machine Learning Thomas G. Dietterich

AAAI 1990 Proceedings of the 8th National Conference on Artificial Intelligence. Boston, Massachusetts, USA, July 29 - August 3, 1990, 2 Volumes Howard E. Shrobe, Thomas G. Dietterich, William R. Swartout

MLJ 1989 A Study of Explanation-Based Methods for Inductive Learning Nicholas S. Flann, Thomas G. Dietterich

ICML 1989 Limitations on Inductive Learning Thomas G. Dietterich

MLJ 1989 News and Notes Thomas G. Dietterich

ICML 1989 What Good Are Experiments? Ritchey A. Ruff, Thomas G. Dietterich

AAAI 1988 An Efficient ATMS for Equivalence Relations Caroline N. Koff, Nicholas S. Flann, Thomas G. Dietterich

MLJ 1988 News and Notes Thomas G. Dietterich

AAAI 1987 Forward Chaining Logic Programming with the ATMS Nicholas S. Flann, Thomas G. Dietterich, Dan R. Corpon

MLJ 1987 News and Notes Thomas G. Dietterich

MLJ 1987 News and Notes Thomas G. Dietterich

MLJ 1987 News and Notes Thomas G. Dietterich

MLJ 1987 News and Notes Thomas G. Dietterich

MLJ 1986 Learning at the Knowledge Level Thomas G. Dietterich

MLJ 1986 News and Notes Thomas G. Dietterich

MLJ 1986 News and Notes Thomas G. Dietterich, Nicholas S. Flann, David C. Wilkins

MLJ 1986 News and Notes Yves Kodratoff, Gheorghe Tecuci, Thomas G. Dietterich

AAAI 1986 Selecting Appropriate Representations for Learning from Examples Nicholas S. Flann, Thomas G. Dietterich

AAAI 1984 Learning About Systems That Contain State Variables Thomas G. Dietterich

AAAI 1980 Applying General Induction Methods to the Card Game Eleusis Thomas G. Dietterich

IJCAI 1979 Learning and Generalization of Characteristic Descriptions: Evaluation Criteria and Comparative Review of Selected Methods Thomas G. Dietterich, Ryszard S. Michalski