Thomas G. Dietterich

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Discovering Patterns in Sequences of Events

Thomas G. Dietterich, Ryszard S. Michalski, R. Michalski, 1985, Artif. Intell..

Prune-and-Score: Learning for Greedy Coreference Resolution

Thomas G. Dietterich, Xiaoli Z. Fern, Chao Ma, 2014, EMNLP.

What Good Are Experiments?

Thomas G. Dietterich, Ritchey A. Ruff, R. Ruff, 1989, ML.

Stabilizing Value Function Approximation with the BFBP Algorithm

Xin Wang, Thomas G. Dietterich, 2001, NIPS.

ℋC-search for structured prediction in computer vision

Thomas G. Dietterich, Michael Lam, Janardhan Rao Doppa, 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

SRL2004 ICML 2004 Workshop on Statistical Relational Learning and its Connections to Other Fields

Thomas G. Dietterich, Lise Getoor, L. Getoor, 2004 .

Dictionary-free categorization of very similar objects via stacked evidence trees

Thomas G. Dietterich, Linda G. Shapiro, Eric N. Mortensen, 2009, CVPR.

Readings in Machine Learning

Thomas G. Dietterich, Jude Shavlik, Thomas E. Deitterich, 1991 .

Active Imitation Learning via Reduction to I.I.D. Active Learning

Thomas G. Dietterich, Alan Fern, Kshitij Judah, 2012, AAAI Fall Symposium: Robots Learning Interactively from Human Teachers.

Hybrid Computational Models for Skill Acquisition

Thomas G. Dietterich, Prasad Tadepalli, 1998 .

A family of large margin linear classifiers and its application in dynamic environments

Thomas G. Dietterich, Jianqiang Shen, 2009 .

07161 Abstracts Collection -- Probabilistic, Logical and Relational Learning - A Further Synthesis

Luc De Raedt, Thomas G. Dietterich, Kristian Kersting, 2007, Probabilistic, Logical and Relational Learning - A Further Synthesis.

Learning to Predict Sequences

Thomas G. Dietterich, Ryszard S. Michalski, R. Michalski, 1985 .

Active EM to reduce noise in activity recognition

Thomas G. Dietterich, Jianqiang Shen, Jianqiang Shen, 2007, IUI '07.

News and Notes

Thomas G. Dietterich, 1986, Mach. Learn..

Low Bias Bagged Support Vector Machines

Giorgio Valentini, Thomas G. Dietterich, G. Valentini, 2003, ICML.

AAAI-07 Workshop Reports

James A. Hendler, Huajun Chen, Alfred Kobsa, 2007, AI Mag..

Integrating Learning from Examples into the Search for Diagnostic Policies

Thomas G. Dietterich, Valentina Bayer Zubek, V. Zubek, 2005, J. Artif. Intell. Res..

Evaluating Online Text Classification Algorithms for Email Prediction in TaskTracer

Thomas G. Dietterich, Victoria Keiser, 2009 .

Just enough die-level test: optimizing ic test via machine learning and decision theory

Thomas G. Dietterich, Tony Fountain, Tony Fountain, 1998 .

Pruning Adaptive Boosting

Thomas G. Dietterich, Dragos D. Margineantu, D. Margineantu, 1997, ICML.

Training conditional random fields via gradient tree boosting

Thomas G. Dietterich, Yaroslav Bulatov, Adam Ashenfelter, 2004, ICML.

Learning About Systems That Contain State Variables

Thomas G. Dietterich, 1984, AAAI.

Principal Curvature-Based Region Detector for Object Recognition

Thomas G. Dietterich, Linda G. Shapiro, Wei Zhang, 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

Fewer clicks and less frustration: reducing the cost of reaching the right folder

Thomas G. Dietterich, Jonathan L. Herlocker, Xinlong Bao, 2006, IUI '06.

Learning Greedy Policies for the Easy-First Framework

Thomas G. Dietterich, Xiaoli Z. Fern, Chao Ma, 2015, AAAI.

Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms

Thomas G. Dietterich, 1998, Neural Computation.

Editorial

Thomas G. Dietterich, 1993, Machine Learning.

The Divide-and-Conquer Manifesto

Thomas G. Dietterich, 2000, ALT.

05051 Executive Summary - Probabilistic, Logical and Relational Learning - Towards a Synthesis

Luc De Raedt, Thomas G. Dietterich, Stephen Muggleton, 2005, Probabilistic, Logical and Relational Learning.

Transductive Optimization of Top k Precision

Thomas G. Dietterich, Zhi-Hua Zhou, Nan Li, 2015, IJCAI.

Machine Learning for Real-Time Decision Making

Thomas G. Dietterich, 2001 .

Bias-Variance Analysis and Ensembles of SVM

Giorgio Valentini, Thomas G. Dietterich, G. Valentini, 2002, Multiple Classifier Systems.

News and Notes

Thomas G. Dietterich, 1987, Mach. Learn..

Batch Value Function Approximation via Support Vectors

Xin Wang, Thomas G. Dietterich, Xin Wang, 2001, NIPS.

On Learning More Concepts

Thomas G. Dietterich, Hussein Almuallim, H. Almuallim, 1992, ML.

Machine learning for cognitive networks : technology assessment and research challenges

Thomas G. Dietterich, Pat Langley, P. Langley, 2003 .

News and Notes

Thomas G. Dietterich, 1904, Journal of the Neurological Sciences.

Steps Toward Robust Artificial Intelligence

Thomas G. Dietterich, 2017, AI Mag..

Constructing High-Accuracy Letter-to-Phoneme Rules with Machine Learning

Thomas G. Dietterich, Ghulum Bakiri, 2001 .

DARPA's Role in Machine Learning

Thomas G. Dietterich, Joshua Alspector, J. Alspector, 2020, AI Mag..

Image features and learning algorithms for biological, generic and social object recognition

Thomas G. Dietterich, Wei Zhang, 2009 .

A data representation for collaborative mechanical design

Thomas G. Dietterich, David G. Ullman, Richard L. Nagy, 1992 .

Forward Chaining Logic Programming with the ATMS

Thomas G. Dietterich, Nicholas S. Flann, Dan R. Corpon, 1987, AAAI.

Selecting Appropriate Representations for Learning from Examples

Thomas G. Dietterich, Nicholas S. Flann, N. Flann, 1986, AAAI.

The use of provenance in information retrieval

Thomas G. Dietterich, Simone Stumpf, Erin Fitzhenry, 2007 .

Detecting insider threats in a real corporate database of computer usage activity

Thomas G. Dietterich, Danai Koutra, Christos Faloutsos, 2013, KDD.

Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs

Thomas G. Dietterich, Ghulum Bakiri, Ghulum Bakiri, 1991, AAAI.

Machine Learning for Sequential Data: A Review

Thomas G. Dietterich, 2002, SSPR/SPR.

Applying General Induction Methods to the Card Game Eleusis

Thomas G. Dietterich, 1980, AAAI.

Interactive visualization for testing Markov Decision Processes: MDPVIS

Thomas G. Dietterich, Ronald A. Metoyer, Sean McGregor, 2017, J. Vis. Lang. Comput..

Inferring Strategies from Limited Reconnaissance in Real-time Strategy Games

Thomas G. Dietterich, Alan Fern, Jesse Hostetler, 2012, UAI.

Sample-Based Tree Search with Fixed and Adaptive State Abstractions

Thomas G. Dietterich, Alan Fern, Jesse Hostetler, 2017, J. Artif. Intell. Res..

Machine Learning in Ecosystem Informatics

Thomas G. Dietterich, 2007, ALT.

Applying machine learning for prediction, recommendation, and integration

Thomas G. Dietterich, Xinlong Bao, Xinlong Bao, 2009 .

A family of large margin linear classifiers and its application in dynamic environments

Thomas G. Dietterich, Jianqiang Shen, Jianqiang Shen, 2009, Stat. Anal. Data Min..

Support Vectors for Reinforcement Learning

Thomas G. Dietterich, Xin Wang, Xin Wang, 2001, PKDD.

Machine Learning Methods for High Level Cyber Situation Awareness

Thomas G. Dietterich, Jianqiang Shen, Victoria Keiser, 2010, Cyber Situational Awareness.

Overfitting and undercomputing in machine learning

Thomas G. Dietterich, 1995, CSUR.

A Multi-agent Architecture Integrating Learning and Fuzzy Techniques for Landmark-Based Robot Navigation

Thomas G. Dietterich, Dídac Busquets, Ramón López de Mántaras, 2002, CCIA.

A Hierarchical Object Recognition System Based on Multi-scale Principal Curvature Regions

Thomas G. Dietterich, Wei Zhang, Eric N. Mortensen, 2006, 18th International Conference on Pattern Recognition (ICPR'06).

The TaskTracker System

Thomas G. Dietterich, Simone Stumpf, Jianqiang Shen, 2005, AAAI.

Rise of concerns about AI

Thomas G. Dietterich, Eric Horvitz, E. Horvitz, 2015, Commun. ACM.

News and notes

Thomas G. Dietterich, 1991, Journal of the Neurological Sciences.

Real-Time Detection of Task Switches of Desktop Users

Thomas G. Dietterich, Jianqiang Shen, Lida Li, 2007, IJCAI.

Three New Algorithms to Solve N-POMDPs

Thomas G. Dietterich, Yann Dujardin, Iadine Chades, 2017, AAAI.

Efficient Algorithms for Identifying Relevant Features

Thomas G. Dietterich, Hussein Almuallim, H. Almuallim, 1992 .

State Abstraction in MAXQ Hierarchical Reinforcement Learning

Thomas G. Dietterich, 1999, NIPS.

Collective Graphical Models

Thomas G. Dietterich, Daniel Sheldon, D. Sheldon, 2011, NIPS.

Abstraction, Reformulation, and Approximation

Thomas G. Dietterich, Toby Walsh, Berthe Y. Choueiry, 2000, Lecture Notes in Computer Science.

News and Notes

Thomas G. Dietterich, 1971, Mach. Learn..

Towards learning rules from natural texts

Thomas G. Dietterich, Xiaoli Z. Fern, Janardhan Rao Doppa, 2010, HLT-NAACL 2010.

KI-LEARN: Knowledge-Intensive Learning Methods for Knowledge-Rich/Data-Poor Domains

Thomas G. Dietterich, Alan Fern, Jianqiang Shen, 2006 .

05051 Abstracts Collection - Probabilistic, Logical and Relational Learning - Towards a Synthesis

Luc De Raedt, Thomas G. Dietterich, Stephen Muggleton, 2005, Probabilistic, Logical and Relational Learning.

Probabilistic models for quality control in environmental sensor networks

Thomas G. Dietterich, Ethan W. Dereszynski, 2012 .

PAC Optimal Planning for Invasive Species Management: Improved Exploration for Reinforcement Learning from Simulator-Defined MDPs

Thomas G. Dietterich, Mark Crowley, Majid Alkaee Taleghan, 2013, AAAI.

Approximate Inference in Collective Graphical Models

Thomas G. Dietterich, Tao Sun, Akshat Kumar, 2013, ICML.

Learning first-order probabilistic models with combining rules

Thomas G. Dietterich, Sriraam Natarajan, Alan Fern, 2005, Annals of Mathematics and Artificial Intelligence.

Pruning Improves Heuristic Search for Cost-Sensitive Learning

Thomas G. Dietterich, Valentina Bayer Zubek, V. Zubek, 2002, ICML.

Learning Rules from Incomplete Examples via Implicit Mention Models

Thomas G. Dietterich, Jed Irvine, Xiaoli Z. Fern, 2011, ACML.

Compass: A shape-based machine learning tool for drug design

Thomas G. Dietterich, Richard H. Lathrop, Tomás Lozano-Pérez, 1994, J. Comput. Aided Mol. Des..

Cyber SA: Situational Awareness for Cyber Defense

John Yen, Sushil Jajodia, Thomas G. Dietterich, 2010, Cyber Situational Awareness.

The Role of the Critic in Learning Systems

Thomas G. Dietterich, B. G. Buchanan, B. Buchanan, 1984 .

Spatiotemporal Models for Data-Anomaly Detection in Dynamic Environmental Monitoring Campaigns

Thomas G. Dietterich, Ethan W. Dereszynski, 2011, TOSN.

Methods for cost-sensitive learning

Thomas G. Dietterich, Dragos D. Margineantu, D. Margineantu, 2002 .

Open Category Detection with PAC Guarantees pdf

Thomas G. Dietterich, Alan Fern, Si Liu, 2018, ICML.

Gaussian Approximation of Collective Graphical Models

Thomas G. Dietterich, Daniel Sheldon, Li-Ping Liu, 2014, ICML.

High-Performance Job-Shop Scheduling With A Time-Delay TD-lambda Network

Thomas G. Dietterich, Wei Zhang, Wei Zhang, 1995, NIPS.

Learning Probabilistic Behavior Models in Real-Time Strategy Games

Thomas G. Dietterich, Alan Fern, Jesse Hostetler, 2011, AIIDE.

Fast Optimization of Wildfire Suppression Policies with SMAC pdf

Thomas G. Dietterich, Ronald A. Metoyer, Sean McGregor, 2017, ArXiv.

Sequential Feature Explanations for Anomaly Detection

Thomas G. Dietterich, Weng-Keen Wong, Alan Fern, 2019, ACM Trans. Knowl. Discov. Data.

Explanation-Based Learning and Reinforcement Learning: A Unified View

Thomas G. Dietterich, Nicholas S. Flann, N. Flann, 1995, Machine Learning.

Probabilistic, Logical and Relational Learning - A Further Synthesis, 15.04. - 20.04.2007

Luc De Raedt, Thomas G. Dietterich, Kristian Kersting, 2008, Probabilistic, Logical and Relational Learning - A Further Synthesis.

Machine Learning in Ecosystem Informatics

Thomas G. Dietterich, 2007, Discovery Science.

The MAXQMethod for Hierarchical Reinforcement Learning

Thomas G. Dietterich, 2012 .

A Conditional Multinomial Mixture Model for Superset Label Learning

Thomas G. Dietterich, Li-Ping Liu, Li-Ping Liu, 2012, NIPS.

Applying the Waek Learning Framework to Understand and Improve C4.5

Thomas G. Dietterich, Yishay Mansour, Michael Kearns, 1996, ICML.

Improving Intelligent Assistants for Desktop Activities

Thomas G. Dietterich, Margaret M. Burnett, Simone Stumpf, 2007, Interaction Challenges for Intelligent Assistants.

A Unified Approach to Abductive Inference

Henry Kautz, Thomas G. Dietterich, Joshua B. Tenenbaum, 2014 .

An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Thomas G. Dietterich, 2000, Machine Learning.

FolderPredictor: Reducing the cost of reaching the right folder

Thomas G. Dietterich, Xinlong Bao, Xinlong Bao, 2011, TIST.

Factoring Exogenous State for Model-Free Monte Carlo pdf

Thomas G. Dietterich, Ronald A. Metoyer, Sean McGregor, 2017, ArXiv.

Learning Boolean Concepts in the Presence of Many Irrelevant Features

Thomas G. Dietterich, Hussein Almuallim, H. Almuallim, 1994, Artif. Intell..

Automatic discovery and transfer of MAXQ hierarchies

Thomas G. Dietterich, Prasad Tadepalli, Soumya Ray, 2008, ICML '08.

Editorial Exploratory research in machine learning

Thomas G. Dietterich, 1990, Machine Learning.

Gaussian process regression with mismatched models

Thomas G. Dietterich, Zoubin Ghahramani, Suzanna Becker, 2002, Neural Information Processing Systems.

News and notes

Thomas G. Dietterich, 1994, Journal of the Neurological Sciences.

Editorial

Thomas G. Dietterich, 1992, Machine Learning.

The MAXQ Method for Hierarchical Reinforcement Learning

Thomas G. Dietterich, 1998, ICML.

Conditional mixture models for precipitation data quality control

Thomas G. Dietterich, Tadesse ZeMicheal, Tadesse Zemicheal, 2020, COMPASS.

A Comparative Review of Selected Methods for Learning from Examples

Thomas G. Dietterich, Ryszard S. Michalski, R. Michalski, 1983 .

Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition

Thomas G. Dietterich, 1999, J. Artif. Intell. Res..

Computational sustainability

Thomas G. Dietterich, Steve Kelling, Warren B. Powell, 2011, AI Mag..

Facilitating testing and debugging of Markov Decision Processes with interactive visualization

Thomas G. Dietterich, Ronald A. Metoyer, Sean McGregor, 2015, 2015 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC).

News and notes

Thomas G. Dietterich, 1993, Journal of the Neurological Sciences.

Locally Adaptive Nearest Neighbor Algorithms

Thomas G. Dietterich, Dietrich Wettschereck, D. Wettschereck, 1993, NIPS.

Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods

Giorgio Valentini, Thomas G. Dietterich, G. Valentini, 2004, J. Mach. Learn. Res..

News and notes

Thomas G. Dietterich, 1989, Mach. Learn..

A POMDP Approximation Algorithm That Anticipates the Need to Observe

Thomas G. Dietterich, Valentina Bayer Zubek, V. Zubek, 2000, PRICAI.

Incorporating Feedback into Tree-based Anomaly Detection pdf

Thomas G. Dietterich, Weng-Keen Wong, Shubhomoy Das, 2017, ArXiv.

Deep Reading and Learning

Thomas G. Dietterich, Xiaoli Z. Fern, Prasad Tadepalli, 2017 .

Advances in neural information processing systems : proceedings of the ... conference

Thomas G. Dietterich, Richard P. Lippmann, Klaus-Robert Müller, 1989 .

News and notes

Thomas G. Dietterich, 1987, Mach. Learn..

The TaskTracer system

Thomas G. Dietterich, Simone Stumpf, Jianqiang Shen, 2005, AAAI 2005.

Anomaly detection in the presence of missing values for weather data quality control

Thomas G. Dietterich, Tadesse Zemicheal, Tadesse Zemicheal, 2018, COMPASS.

High-Performance Job-Shop Scheduling With A Time-Delay TD(λ) Network

Thomas G. Dietterich, Wei Zhang, Wei Zhang, 1995, NIPS 1995.

Improving the Performance of Radial Basis Function Networks by Learning Center Locations

Thomas G. Dietterich, Dietrich Wettschereck, D. Wettschereck, 1991, NIPS.

State Aggregation in Monte Carlo Tree Search

Thomas G. Dietterich, Alan Fern, Jesse Hostetler, 2014, AAAI.

Learning MDP Action Models Via Discrete Mixture Trees

Thomas G. Dietterich, Michael Wynkoop, 2008, ECML/PKDD.

Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar

Thomas G. Dietterich, Steve Kelling, Jed Irvine, 2013, AAAI.

Improving Automated Email Tagging with Implicit Feedback

Thomas G. Dietterich, Michael Slater, Mohammad S. Sorower, 2015, UIST.

Discovering frequent work procedures from resource connections

Thomas G. Dietterich, Jianqiang Shen, Erin Fitzhenry, 2009, IUI.

Machine-Learning Research

Thomas G. Dietterich, 1997, AI Mag..

A Unifying Review of Deep and Shallow Anomaly Detection pdf

Thomas G. Dietterich, Wojciech Samek, Klaus-Robert Muller, 2020, Proceedings of the IEEE.

Interacting meaningfully with machine learning systems: Three experiments

Thomas G. Dietterich, Weng-Keen Wong, Margaret M. Burnett, 2009, Int. J. Hum. Comput. Stud..

Knowledge Compilation to Speed Up Numerical Optimisation

Thomas G. Dietterich, Giuseppe Cerbone, G. Cerbone, 1991, AI*IA.

Haar Random Forest Features and SVM Spatial Matching Kernel for Stonefly Species Identification

Thomas G. Dietterich, Linda G. Shapiro, Gonzalo Martínez-Muñoz, 2010, 2010 20th International Conference on Pattern Recognition.

Reinforcement Learning Via Practice and Critique Advice

Thomas G. Dietterich, Alan Fern, Saikat Roy, 2010, AAAI.

Spatial interactions and optimal forest management on a fire-threatened landscape

Thomas G. Dietterich, Claire A. Montgomery, Christopher J. Lauer, 2017 .

Incorporating Boosted Regression Trees into Ecological Latent Variable Models

Thomas G. Dietterich, Rebecca A. Hutchinson, Li-Ping Liu, 2011, AAAI.

In Advances in Neural Information Processing Systems 12

Thomas G. Dietterich, Simon M. Becker, Z. Ghahramani Eds, 1991, NIPS 1991.

Learning first-order probabilistic models with combining rules

Thomas G. Dietterich, Sriraam Natarajan, Alan Fern, 2009, Annals of Mathematics and Artificial Intelligence.

Deep Anomaly Detection with Outlier Exposure pdf

Thomas G. Dietterich, Dan Hendrycks, Mantas Mazeika, 2018, ICLR.

Action Refinement in Reinforcement Learning by Probability Smoothing

Thomas G. Dietterich, Dídac Busquets, Ramón López de Mántaras, 2002, ICML.

Knowledge Compilation to Speed Up Numerical Optimization

Thomas G. Dietterich, Giuseppe Cerbone, 1991, ML.

Machine-Learning Research Four Current Directions

Thomas G. Dietterich, 1997 .

Guiding Scientific Discovery with Explanations Using DEMUD

Thomas G. Dietterich, David R. Thompson, Martha S. Gilmore, 2013, AAAI.

Memory-Based Methods for Regression and Classification

Andrew W. Moore, Thomas G. Dietterich, Christopher G. Atkeson, 1993, NIPS.

News and notes

Thomas G. Dietterich, 1945, Journal of the Neurological Sciences.

Transfer Learning with an Ensemble of Background Tasks

Thomas G. Dietterich, Leslie Pack Kaelbling, Zvika Marx, 2005, NIPS 2005.

News and notes

Thomas G. Dietterich, 1987, Mach. Learn..

News and Notes

Thomas G. Dietterich, David C. Wilkins, Nicholas S. Flann, 1986, Machine Learning.

Letter to the Editor: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter

Thomas G. Dietterich, Eric Horvitz, Francesca Rossi, 2015, AI Mag..

Machine Learning and Ecosystem Informatics: Challenges and Opportunities

Thomas G. Dietterich, 2009, ACML.

Editors. Advances in Neural Information Processing Systems

Thomas G. Dietterich, Zoubin Ghahramani, Suzanna Becker, 2002 .

Learning with Many Irrelevant Features

Thomas G. Dietterich, Hussein Almuallim, H. Almuallim, 1991, AAAI.

TaskTracer: a desktop environment to support multi-tasking knowledge workers

Thomas G. Dietterich, Lida Li, Jonathan L. Herlocker, 2005, IUI.

A Reinforcement Learning Approach to job-shop Scheduling

Wei Zhang, Thomas G. Dietterich, Wei Zhang, 1995, IJCAI.

Learning non-redundant codebooks for classifying complex objects

Thomas G. Dietterich, Wei Zhang, Xiaoli Z. Fern, 2009, ICML '09.

An Efficient ATMS for Equivalence Relations

Thomas G. Dietterich, Nicholas S. Flann, Caroline N. Koff, 1988, AAAI.

Explanation-Based Learning and Reinforcement Learning: A Unified View

Thomas G. Dietterich, Nicholas S. Flann, N. Flann, 1995, Machine-mediated learning.

Integrating Multiple Learning Components through Markov Logic

Thomas G. Dietterich, Xinlong Bao, 2008, AAAI.

Learning Scripts as Hidden Markov Models

Thomas G. Dietterich, Xiaoli Z. Fern, Janardhan Rao Doppa, 2014, AAAI.

Inductive Learning of Structural Descriptions: Evaluation Criteria and Comparative Review of Selected Methods

Thomas G. Dietterich, Ryszard S. Michalski, R. Michalski, 1981, Artif. Intell..

News and notes

Thomas G. Dietterich, Yves Kodratoff, Gheorghe Tecuci, 1986, Mach. Learn..

Limitations on Inductive Learning

Thomas G. Dietterich, 1989, ML.

Title: Activity Recognition in Desktop Environments

Thomas G. Dietterich, 2009 .

Automated Insect Identification through Concatenated Histograms of Local Appearance Features

Thomas G. Dietterich, Linda G. Shapiro, Wei Zhang, 2007, WACV.

Learning at the Knowledge Level

Thomas G. Dietterich, 1986, Machine Learning.

What is machine learning?

Thomas G. Dietterich, 2020, Archives of Disease in Childhood.

Automatic Discovery and Transfer of Task Hierarchies in Reinforcement Learning

Thomas G. Dietterich, Prasad Tadepalli, Soumya Ray, 2011, AI Mag..

Editorial: New Editorial Board Members

Thomas G. Dietterich, 2004, Machine Learning.

Machine Learning in Engineering Automation

Thomas G. Dietterich, Brian Falkenhainer, Richard J. Doyle, 1991, ML.

Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning pdf

Thomas G. Dietterich, Zhitang Chen, George Trimponias, 2018, ICML.

News and notes

Thomas G. Dietterich, 1987, Journal of the Neurological Sciences.

Learning cost-sensitive diagnostic policies from data

Thomas G. Dietterich, Valentina Bayer Zubek, 2003 .

The EG project: recent progress

Thomas G. Dietterich, 1986 .

Solving the Multiple Instance Problem with Axis-Parallel Rectangles

Thomas G. Dietterich, Richard H. Lathrop, Tomás Lozano-Pérez, 1997, Artif. Intell..

Finite Sample Complexity of Rare Pattern Anomaly Detection

Thomas G. Dietterich, Shubhomoy Das, Alan Fern, 2016, UAI.

Three-quarter Sibling Regression for Denoising Observational Data pdf

Thomas G. Dietterich, Tao Sun, Daniel Sheldon, 2019, IJCAI.

Toward harnessing user feedback for machine learning

Thomas G. Dietterich, Margaret M. Burnett, Simone Stumpf, 2007, IUI '07.

Probabilistic Models for Anomaly Detection in Remote Sensor Data Streams

Thomas G. Dietterich, Ethan W. Dereszynski, 2012, 1206.5250.

Achieving High-Accuracy Text-to-Speech with Machine Learning

Thomas G. Dietterich, Ghulum Bakiri, Ghulum Bakiri, 1997 .

Active lmitation learning: formal and practical reductions to I.I.D. learning

Thomas G. Dietterich, Alan Fern, Prasad Tadepalli, 2014, J. Mach. Learn. Res..

News and Notes

Thomas G. Dietterich, 1987, Mach. Learn..

A Comparison of ID3 and Backpropagation for English Text-To-Speech Mapping

Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri, 2004, Machine Learning.

Improving SVM accuracy by training on auxiliary data sources

Thomas G. Dietterich, Pengcheng Wu, P. Wu, 2004, ICML.

An Ensemble Learning and Problem Solving Architecture for Airspace Management

Santiago Ontañón, James A. Hendler, Thomas G. Dietterich, 2009, IAAI.

Can We Achieve Open Category Detection with Guarantees?

Thomas G. Dietterich, Alan Fern, Si Liu, 2018, AAAI Workshops.

Anomaly Detection Meta-Analysis Benchmarks

Thomas G. Dietterich, Weng-Keen Wong, Shubhomoy Das, 2016 .

Who speaks for AI?

Eric Eaton, Thomas G. Dietterich, Michael Wooldridge, 2016, SIGAI.

Reconstructing Velocities of Migrating Birds from Weather Radar - A Case Study in Computational Sustainability

Thomas G. Dietterich, Steve Kelling, Jed Irvine, 2014, AI Mag..

Error-Correcting Output Coding Corrects Bias and Variance

Thomas G. Dietterich, Eun Bae Kong, E. B. Kong, 1995, ICML.

Hierarchical Explanation-Based Reinforcement Learning

Thomas G. Dietterich, Prasad Tadepalli, Prasad Tadepalli, 1997, ICML.

An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration

Santiago Ontañón, James A. Hendler, Thomas G. Dietterich, 2012, TIST.

PAC optimal MDP planning with application to invasive species management

Thomas G. Dietterich, Mark Crowley, Majid Alkaee Taleghan, 2015, J. Mach. Learn. Res..

A Study of Explanation-Based Methods for Inductive Learning

Thomas G. Dietterich, Nicholas S. Flann, N. Flann, 1989, Machine Learning.

News and Notes

Thomas G. Dietterich, Yves Kodratoff, Gheorghe Tecuci, 1984, Mach. Learn..

Inductive Learning from Preclassified Training Examples

Thomas G. Dietterich, 1990 .

The life and times of files and information: a study of desktop provenance

Thomas G. Dietterich, Carlos Jensen, Eleanor Wynn, 2010, CHI.

Structured machine learning: the next ten years

Thomas G. Dietterich, Stephen Muggleton, Pedro M. Domingos, 2008, Machine Learning.

A comparison of ID3 and backpropagation for English text-to-speech mapping

Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri, 2004, Machine Learning.

α-min: A Compact Approximate Solver For Finite-Horizon POMDPs

Thomas G. Dietterich, Yann Dujardin, Iadine Chades, 2015, IJCAI.

Feedback-Guided Anomaly Discovery via Online Optimization

Thomas G. Dietterich, Alan Fern, David W. Archer, 2018, KDD.

The eBird enterprise: An integrated approach to development and application of citizen science

Thomas G. Dietterich, Steve Kelling, Jeff Gerbracht, 2014 .

An experimental comparison of the nearest-neighbor and nearest-hyperrectangle algorithms

Thomas G. Dietterich, Dietrich Wettschereck, D. Wettschereck, 1995, Machine Learning.

Knowledge Compilation: Bridging the Gap between Specification and Implementation

Thomas G. Dietterich, 1991 .

Facilitating testing and debugging of Markov Decision Processes with interactive visualization

Thomas G. Dietterich, Ronald Metoyer, Sean McGregor, 2015, VL/HCC.

Inverting Grice's Maxims to Learn Rules from Natural Language Extractions

Thomas G. Dietterich, Xiaoli Z. Fern, Janardhan Rao Doppa, 2011, NIPS.

News and notes

Thomas G. Dietterich, 1986, Mach. Learn..

Converting English text to speech: a machine learning approach

Thomas G. Dietterich, Ghulum Bakiri, Ghulum Bakiri, 1991 .

Detecting and correcting user activity switches: algorithms and interfaces

Thomas G. Dietterich, Simone Stumpf, Jed Irvine, 2009, IUI.

Learning from Sparse Data by Exploiting Monotonicity Constraints

Thomas G. Dietterich, Eric Altendorf, Angelo C. Restificar, 2005, UAI.

Robust artificial intelligence and robust human organizations

Thomas G. Dietterich, 2018, Frontiers of Computer Science.

Reinforcement Matching Using Region Context

Thomas G. Dietterich, Linda G. Shapiro, Eric N. Mortensen, 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

Probabilistic, Logical and Relational Learning - Towards a Synthesis, 30. January - 4. February 2005

Luc De Raedt, Thomas G. Dietterich, Stephen Muggleton, 2006, Probabilistic, Logical and Relational Learning.

A hybrid learning system for recognizing user tasks from desktop activities and email messages

Thomas G. Dietterich, Jianqiang Shen, Lida Li, 2006, IUI '06.

Editorial

Thomas G. Dietterich, 1996, Machine Learning.

Learning decision trees for loss minimization in multi-class problems

Thomas G. Dietterich, Dragos D. Margineantu, D. Margineantu, 1999 .

Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3-8, 2001, Vancouver, British Columbia, Canada]

Thomas G. Dietterich, Zoubin Ghahramani, Suzanna Becker, 2001, NIPS.

Learning visual dictionaries and decision lists for object recognition

Thomas G. Dietterich, Wei Zhang, 2008, 2008 19th International Conference on Pattern Recognition.

A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping

Thomas G. Dietterich, Hermann Hild, Ghulum Bakiri, 1990, ML.

Advances in Neural Information Processing Systems 13, Papers from Neural Information Processing Systems (NIPS) 2000, Denver, CO, USA

Thomas G. Dietterich, Volker Tresp, T. K. Leen, 2001, NIPS.

Learnability of the Superset Label Learning Problem

Thomas G. Dietterich, Li-Ping Liu, Li-Ping Liu, 2014, ICML.

A family of large margin linear classifiers and its application in dynamic environments

Thomas G. Dietterich, Jianqiang Shen, Jianqiang Shen, 2009, Stat. Anal. Data Min..

Automated insect identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects

Thomas G. Dietterich, Linda G. Shapiro, Eric N. Mortensen, 2007, 2007 IEEE Workshop on Applications of Computer Vision (WACV '07).

Emergence of Motion-sensitive Neurons Properties by Learning Sparse Code for Natural Moving Images

Thomas G. Dietterich, Volker Tresp, Christophe Giraud-Carrier, 2001 .

Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers

Thomas G. Dietterich, Dragos D. Margineantu, 2000, ICML.

Learning Rules from Incomplete Examples: A Pragmatic Approach

Thomas G. Dietterich, Xiaoli Z. Fern, Janardhan Rao Doppa, 2010, HLT-NAACL 2010.

Machine learning for computational sustainability

Thomas G. Dietterich, Daniel Sheldon, Rebecca A. Hutchinson, 2012, 2012 International Green Computing Conference (IGCC).

Zero-Shot Learning and Detection of Teeth in Images of Bat Skulls

Thomas G. Dietterich, Michael Lam, Xu Hu, 2013, 2013 IEEE International Conference on Computer Vision Workshops.

Efficient Exploration for Constrained MDPs

Thomas G. Dietterich, Majid Alkaee Taleghan, 2018, AAAI Spring Symposia.

Statistical Machine Learning for Large-Scale Optimization

Kee-Eung Kim, Thomas G. Dietterich, Russell Impagliazzo, 2000 .

Learning and Generalization of Characteristic Descriptions: Evaluation Criteria and Comparative Review of Selected Methods

Thomas G. Dietterich, Ryszard S. Michalski, R. Michalski, 1979, IJCAI.

Data mining for manufacturing control: an application in optimizing IC tests

Thomas G. Dietterich, Tony Fountain, Bill Sudyka, 2003 .

A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction

Thomas G. Dietterich, Richard H. Lathrop, Tomás Lozano-Pérez, 1993, NIPS.

Adaptive computation and machine learning

Thomas G. Dietterich, 1998 .

Dictionary-free categorization of very similar objects via stacked evidence trees

Thomas G. Dietterich, Linda G. Shapiro, Eric N. Mortensen, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms

Thomas G. Dietterich, Eun Bae, Eun Bae, 2008 .

Stacked spatial-pyramid kernel: An object-class recognition method to combine scores from random trees

Thomas G. Dietterich, Linda G. Shapiro, Mengzi Zhang, 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).

Machine Learning in Ecosystem Informatics and Sustainability

Thomas G. Dietterich, 2009, IJCAI.

Support Vectors for Reinforcement Learning

Thomas G. Dietterich, Xin Wang, 2001, ECML.

Incorporating Expert Feedback into Active Anomaly Discovery

Thomas G. Dietterich, Weng-Keen Wong, Shubhomoy Das, 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

Multiple Classifier Systems

Thomas G. Dietterich, 2000, Lecture Notes in Computer Science.

Learning and Inductive Inference

Thomas G. Dietterich, Kenneth Clarkson, Bob London, 1982 .

Recovery from Interruptions: Knowledge Workers' Strategies, Failures and Envisioned Solutions

Thomas G. Dietterich, Simone Stumpf, Jonathan L. Herlocker, 2005 .

Reinforcement learning in scheduling

Thomas G. Dietterich, Prasad Tadepalli, Wei Zhang, 1994 .

A model of the mechanical design process based on empirical data

Thomas G. Dietterich, David G. Ullman, Larry A. Stauffer, 1988, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

Predicting User Tasks : I Know What You ’ re Doing !

Thomas G. Dietterich, Simone Stumpf, Jianqiang Shen, 2005 .

Systematic construction of anomaly detection benchmarks from real data pdf

Thomas G. Dietterich, Weng-Keen Wong, Shubhomoy Das, 2013, ODD '13.

Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations

Thomas G. Dietterich, Dan Hendrycks, Dan Hendrycks, 2018, 1807.01697.

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations pdf

Thomas G. Dietterich, Dan Hendrycks, Dan Hendrycks, 2018, ICLR.

Exploiting functional vocabularies to learn structural descriptions

Thomas G. Dietterich, Nicholas S. Flann, 1986 .

A Divide and Conquer Approach to Learning from Prior Knowledge

Thomas G. Dietterich, Eric Chown, E. Chown, 2000, ICML.

Knowledge Compilation: A Symposium

Thomas G. Dietterich, Ashok K. Goel, Chris Tong, 1991, IEEE Expert.

Model-based Policy Gradient Reinforcement Learning

Xin Wang, Thomas G. Dietterich, Xin Wang, 2003, ICML.

News and notes

Thomas G. Dietterich, 1987, Mach. Learn..

Learning to Detect Basal Tubules of Nematocysts in SEM Images

Thomas G. Dietterich, Michael Lam, Xu Hu, 2013, 2013 IEEE International Conference on Computer Vision Workshops.

News and notes

Thomas G. Dietterich, 1988, Mach. Learn..

News and Notes

Thomas G. Dietterich, 1987, Mach. Learn..

Design of an Automated System for Imaging and Sorting Soil Mesofauna

Thomas G. Dietterich, Linda G. Shapiro, Robert Paasch, 2011 .

Managing Fragmented Fire-Threatened Landscapes with Spatial Externalities

Thomas G. Dietterich, Claire A. Montgomery, Christopher J. Lauer, 2020, Forest Science.

Mining IC test data to optimize VLSI testing

Thomas G. Dietterich, Tony Fountain, Bill Sudyka, 2000, KDD '00.

Solving K-MDPs

Olivier Buffet, Thomas G. Dietterich, Jonathan Ferrer-Mestres, 2020, ICAPS.

An Overview of MAXQ Hierarchical Reinforcement Learning

Thomas G. Dietterich, 2000, SARA.

Solving Multiclass Learning Problems via Error-Correcting Output Codes

Thomas G. Dietterich, Ghulum Bakiri, Ghulum Bakiri, 1994, J. Artif. Intell. Res..

The Handbook of Brain Theory and Neural Networks

Thomas G. Dietterich, 2002 .

News and notes

Thomas G. Dietterich, 1978, Journal of the Neurological Sciences.

Progressive Abstraction Refinement for Sparse Sampling

Thomas G. Dietterich, Alan Fern, Jesse Hostetler, 2015, UAI.

Human Computation

Thomas G. Dietterich, William W. Cohen, William Cohen, 2011, Synthesis Lectures on Artificial Intelligence and Machine Learning.

MDPVIS: An Interactive Visualization for Testing Markov Decision Processes

Thomas G. Dietterich, Ronald A. Metoyer, Sean McGregor, 2015, AAAI Fall Symposia.

Theory and Practice of Machine Learning (Dagstuhl Seminar 9702)

Thomas G. Dietterich, Hans Ulrich Simon, Wolfgang Maass, 2021 .

Confidence Calibration for Domain Generalization under Covariate Shift pdf

Thomas G. Dietterich, Ajay Divakaran, Yi Yao, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

A Unifying Review of Deep and Shallow Anomaly Detection

Thomas G. Dietterich, Wojciech Samek, Klaus-Robert Muller, 2020, Proceedings of the IEEE.

Theory and Praxis of Machine Learning (Dagstuhl Seminar 9426)

Thomas G. Dietterich, Hans Ulrich Simon, Manfred K. Warmuth, 2021 .

Deep Convolution for Irregularly Sampled Temporal Point Clouds pdf

Thomas G. Dietterich, Alan Fern, Li Fuxin, 2021, ArXiv.

Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA)

Thomas G. Dietterich, Guansong Pang, Anton van den Hengel, 2021, KDD.

A study of distance-based machine learning algorithms

Thomas G. Dietterich, D. Wettschereck, 1994 .

Purposive Behavior Acqui- Sition for a Real Robot by Vision-based Reinforcement Learning. a Mulit- Agent Architecture Integrating Learning and Fuzzy Techniques for Landmark-based

Thomas G. Dietterich, Burgard, A. Cremers, .

Why Error Correcting Output Coding Works

Thomas G. Dietterich, Eun Bae, 1994 .

Efficient Value Function Approximation Using Regression Trees

Thomas G. Dietterich, Xin Wang, 1999 .

Improved Class Probability Estimates from Decision Tree Models

Thomas G. Dietterich, D. Margineantu, 2003 .

State Abstraction in Maxq Hierarchical Reinforcement Learning Category: Reinforcement Learning and Control Preference: Oral

Thomas G. Dietterich, 2000 .

Learning at the knowledge level

Thomas G. Dietterich, 2004, Machine Learning.

Learning Rules from Incomplete Examples via a Probabilistic Mention Model

Thomas G. Dietterich, Xiaoli Z. Fern, Janardhan Rao Doppa, 2011 .

The Role of Restoration and Key Ecological Invasion Mechanisms in Optimal Spatial-Dynamic Management of Invasive Species

Thomas G. Dietterich, Majid Alkaee Taleghan, H. Albers, 2018, Ecological Economics.

Allowing a wildfire to burn: estimating the effect on future fire suppression costs

Thomas G. Dietterich, Claire A. Montgomery, Rachel Houtman, 2013 .

An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms

Thomas G. Dietterich, D. Wettschereck, 1995, Machine Learning.

A Study of Explanation-Based Methods for

Thomas G. Dietterich, N. Flann, 1989 .

The Test Incorporation Theory of Problem Solving

Thomas G. Dietterich, James S. Bennett, 1986 .

Automated processing and identification of benthic invertebrate samples

Thomas G. Dietterich, Eric N. Mortensen, Wei Zhang, 2010, Journal of the North American Benthological Society.

An aquatic insect imaging system to automate insect classification

Thomas G. Dietterich, Eric N. Mortensen, L. Shapiro, 2008 .

Two Algorithms for Transfer Learning

Thomas G. Dietterich, M. Rosenstein, L. Kaelbling, .

Map Misclassification Can Cause Large Errors in Landscape Pattern Indices: Examples from Habitat Fragmentation

Thomas G. Dietterich, William T. Langford, W. Cohen, 2006, Ecosystems.

Gradient Tree Boosting for Training Conditional Random Fields

Thomas G. Dietterich, A. Ashenfelter, Guohua Hao, 2008 .

Evaluating wildland fire liability standards – does regulation incentivise good management?

Thomas G. Dietterich, Claire A. Montgomery, Christopher J. Lauer, 2020 .

AN ABSTRACT OF THE DISSERTATION OF Guohua Hao for the degree of Doctor of Philosophy in Computer Science presented on July 21, 2009. Title: Efficient Training and Feature Induction in Sequential Supervised Learning Abstract approved:

Thomas G. Dietterich, Guohua Hao, 2009 .

Machine Learning 1

Thomas G. Dietterich, 1996 .

Pattern Recognition for Ecological Science and Environmental Monitoring: An Initial Report

Thomas G. Dietterich, Eric N. Mortensen, Wei Zhang, 2006 .

Toward Visualization Methods for Interactive Improvement of MDP Specifications

Thomas G. Dietterich, Ronald A. Metoyer, Sean McGregor, 2014 .

Penalized likelihood methods improve parameter estimates in occupancy models

Thomas G. Dietterich, Sarah C. Emerson, Jonathon J. Valente, 2015 .

Multiple-Model Induction in

Thomas G. Dietterich, 2009 .

FORLOG : A Logic-based Architecture for Design

Thomas G. Dietterich, D. Ullman, 1987 .

Learning at the Knowledge Level Dietterich%oregon-state@csnet-relay

Thomas G. Dietterich, A. Newell, 1986 .

Ao Revisited

Thomas G. Dietterich, V. Zubek, 2012 .

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Thomas G. Dietterich, Dan Hendrycks, 2019, ICLR.

Next-generation phenomics for the Tree of Life

Thomas G. Dietterich, Lisa R. Moore, Andrew J. Alverson, 2013, PLoS currents.

2 The Knowledge Level 2 . 1 Newell ’ s description of the knowledge level

Thomas G. Dietterich, 1986 .

Varieties of Operationality

Thomas G. Dietterich, James S. Bennett, 2022 .

Fundamental Experimental Research in Machine Learning

Thomas G. Dietterich, 1997 .

The Test Incorporation Hypothesis and the Weak Methods

Thomas G. Dietterich, 1986 .

AN ABSTRACT OF THE THESIS OF Michael Lam for the degree of Master of Science in Computer Science presented on May 15, 2014. Title: Object Detection in Biological Images Using a Search-Based Framework Abstract approved:

Thomas G. Dietterich, S. Todorovic, Michael Lam, 2014 .

Induction: Weak but essential

Thomas G. Dietterich, 1986, Behavioral and Brain Sciences.

A Knowledge Base Data Representation for Collaborative Mechanical Design

Thomas G. Dietterich, D. Ullman, Richard L. Nagy, 1991 .

Machine Learning for the Knowledge Plane

Thomas G. Dietterich, Alan Fern, Prasad Tadepalli, 2006 .

A Wrapper Method for Cost-Sensitive Learning via Stratification

Thomas G. Dietterich, Dragos D. Margineantu, D. Margineantu, 2007 .

Two heuristics for solving POMDPs having a delayed need to observe

Thomas G. Dietterich, Valentina Bayer Zubek, V. Zubek, 2004 .

Institutional Knowledge at Singapore Management University Research Collection School Of Information Systems School of Information Systems 2013 Approximate Inference in Collective Graphical Models

Thomas G. Dietterich, D. Sheldon, Tao Sun, 2014 .

Performance Comparison between Human Engineered & Machine Learned Letter-to-sound Rules for English: a Machine Learning Success Story

Thomas G. Dietterich, Ghulum Bakiri, Ghulum Bakiri, 1993 .

A Summary of Machine Learning Papers From IJCAI-85

Thomas G. Dietterich, Nicholas S. Flann, .

Statistical Machine Learning for Large-Scale Optimization Contributors

Thomas G. Dietterich, Wray L. Buntine, R. Impagliazzo, 2022 .

Segmentation of touching insects based on optical flow and NCuts

Thomas G. Dietterich, Sinisa Todorovic, Qing-jie Liu, 2013 .

Algorithms for Identifying Relevant Features

Thomas G. Dietterich, Hussein Almuallim, Dearborn, 1992 .

Learning Rules from Incomplete Examples via Observation Models

Thomas G. Dietterich, Jed Irvine, Xiaoli Z. Fern, 2011 .

Is Fine Grained Classification Different ?

Thomas G. Dietterich, Junyuan Lin, 2013 .

Crowds Replicate Performance of Scientific Experts Scoring Phylogenetic Matrices of Phenotypes

Thomas G. Dietterich, Andrea L. Cirranello, D. Stevenson, 2018, Systematic biology.

1 Design Histories as an Aid to Redesign in Mechanical Engineering —

Thomas G. Dietterich, D. Ullman, 2022 .

Ensemble Methods in Machine Learning

Thomas G. Dietterich, 2000, Multiple Classifier Systems.

Exploiting monotonicity via logistic regression in Bayesian network learning

Thomas G. Dietterich, Angelo C. Restificar, 2013 .

Fast Simulation for Computational Sustainability Sequential Decision Making Problems

Thomas G. Dietterich, Ronald A. Metoyer, Claire A. Montgomery, 2016 .

Learning From Demonstrations via Structured Prediction

Thomas G. Dietterich, Alan Fern, Prasad Tadepalli, 2007 .

The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods

Thomas G. Dietterich, Alexander Guyer, 2022, Pattern Recognit..

The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods

Thomas G. Dietterich, Alexander Guyer, 2022, Pattern Recognition.

Oracle Analysis of Representations for Deep Open Set Detection

Thomas G. Dietterich, Alan Fern, Risheek Garrepalli, 2022, 2209.11350.

Conformal Prediction Intervals for Markov Decision Process Trajectories

Thomas G. Dietterich, Jesse Hostetler, 2022, ArXiv.

The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods

Thomas G. Dietterich, Alexander Guyer, 2022, Pattern Recognit..

The Search for Equilibrium in Markov Games

Thomas G. Dietterich, Amy Greenwald, Ronald Brachman, 2007 .

News and notes

Thomas G. Dietterich, 1945, Journal of the Neurological Sciences.

Will My Robot Achieve My Goals? Predicting the Probability that an MDP Policy Reaches a User-Specified Behavior Target

Thomas G. Dietterich, Alexander Guyer, 2022, ArXiv.

Robust artificial intelligence and robust human organizations

Thomas G. Dietterich, 2018, Frontiers of Computer Science.

A Unifying Review of Deep and Shallow Anomaly Detection

Thomas G. Dietterich, Robert A. Vandermeulen, Jacob R. Kauffmann, 2020, Proceedings of the IEEE.

A Meta-Analysis of the Anomaly Detection Problem

Thomas G. Dietterich, Weng-Keen Wong, Shubhomoy Das, 2015 .

Bioinformatics The Machine Learning Approach 2nd ed.

Thomas G. Dietterich, Thomas Dietterich, 2001 .

Reinforcement Learning with Exogenous States and Rewards

Thomas G. Dietterich, George Trimponias, 2023, ArXiv.

Learning and Reasoning

Thomas G. Dietterich, 2003 .

Oracle Analysis of Representations for Deep Open Set Detection

Thomas G. Dietterich, Alan Fern, Risheek Garrepalli, 2022, arXiv.org.

Hidden Heterogeneity: When to Choose Similarity-Based Calibration

Thomas G. Dietterich, K. Wagstaff, 2022, Trans. Mach. Learn. Res..

Deep Anomaly Detection with Outlier Exposure

Thomas G. Dietterich, Dan Hendrycks, Mantas Mazeika, 2018, ICLR.

Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA)

Thomas G. Dietterich, Longbing Cao, A. van den Hengel, 2021, KDD.

Discovering Anomalies by Incorporating Feedback from an Expert

Thomas G. Dietterich, Alan Fern, Weng-Keen Wong, 2020, ACM Trans. Knowl. Discov. Data.

This Is a Publication of the American Association for Artificial Intelligence

Thomas G. Dietterich, 2022 .

Machine-Learning Research Four Current Directions

Thomas G. Dietterich, 1997 .

Machine Learning Research: Four Current Directions

Thomas G. Dietterich, 2022 .

Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic

Thomas G. Dietterich, Z. Ghahramani, S. Becker, 2001 .

Reinforcement Learning Via Practice and Critique Advice

Thomas G. Dietterich, Alan Fern, Kshitij Judah, 2010, AAAI.

What is machine learning?

Thomas G. Dietterich, 2015, Archives of Disease in Childhood.

An Abstract of the Thesis of Title: a Probabilistic Model for Anomaly Detection in Remote Sensor Streams a Probabilistic Model for Anomaly Detection in Remote Sensor Streams

Thomas G. Dietterich, Ethan W. Dereszynski, Major Professor, 2022 .

What Is Machine Learning?

Thomas G. Dietterich, Arjun Panesar, 2019, Machine Learning and AI for Healthcare.

Supplementary Materials for Confidence Calibration for Domain Generalization under Covariate Shift

Thomas G. Dietterich, Ajay Divakaran, Yi Yao, 2021 .

A family of large margin linear classifiers and its application in dynamic environments

Thomas G. Dietterich, C. Brodley, P. Protopapas, 2009 .

Solving Multiclass Learning Problems via Error-Correcting Output Codes

Thomas G. Dietterich, Ghulum Bakiri, 1994, J. Artif. Intell. Res..

To transfer or not to transfer

Thomas G. Dietterich, M. Rosenstein, L. Kaelbling, 2005, NIPS 2005.

Interactive visualization for testing Markov Decision Processes: MDPVIS

Thomas G. Dietterich, Claire A. Montgomery, Rachel Houtman, 2017, J. Vis. Lang. Comput..

Editorial Exploratory research in machine learning

Thomas G. Dietterich, 1990, Machine Learning.

Optimal Spatial-Dynamic Management of Stochastic Species Invasions

Thomas G. Dietterich, Kim Meyer Hall, Heidi J. Albers, 2018 .

Ensemble Methods in Machine Learning

Thomas G. Dietterich, 2007 .

Next-generation phenomics for the Tree of Life

Thomas G. Dietterich, Lisa R. Moore, Andrew J. Alverson, 2013, PLoS currents.

Multiple Classifier Systems

Thomas G. Dietterich, Gerhard Goos, Juris Hartmanis, 2000, Lecture Notes in Computer Science.

An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, B

Thomas G. Dietterich, 1998 .