Thomas G. Dietterich

Professor

Computer Science

Web Page:

http://eecs.oregonstate.edu/~tgd/

Learn more about Tom Dietterich

Education:

A.B., Mathematics, Oberlin College (Oberlin, Ohio), 1977
with Honors in Mathematics (Probability and Statistics)
M.S., Computer Science, University of Illinois (Urbana, Illinois), 1979
Thesis supervisor: Ryszard S. Michalski
Thesis title: “The Methodology of Knowledge Layers for Inducing Descriptions of Sequentially Ordered Events”
Ph.D., Computer Science, Stanford University (Stanford, California), 1984
Dissertation supervisor: Bruce G. Buchanan
Dissertation title: “Constraint-Propagation Techniques for Theory-Driven Data Interpretation”

Biography:

Thomas G. Dietterich (AB Oberlin College 1977; M.S. University of Illinois 1979; Ph.D. Stanford University 1984) is one of the founders of the field of Machine Learning. Among his research contributions was the application of error-correcting output coding to multiclass classification, the formalization of the multiple-instance problem, the MAXQ framework for hierarchical reinforcement learning, and the development of methods for integrating non-parametric regression trees into probabilistic graphical models (including conditional random fields and latent variable models). Among his writings are Chapter XIV (Learning and Inductive Inference) of the Handbook of Artificial Intelligence, the book Readings in Machine Learning (co-edited with Jude Shavlik), and his frequently-cited review articles Machine Learning Research: Four Current Directions and Ensemble Methods in Machine Learning.

He served as Executive Editor of Machine Learning (1992-98) and helped co-found the Journal of Machine Learning Research. He is currently the editor of the MIT Press series on Adaptive Computation and Machine Learning. He also served as co-editor of the Morgan-Claypool Synthesis Series on Artificial Intelligence and Machine Learning. He has organized several conferences and workshops including serving as Technical Program Co-Chair of the National Conference on Artificial Intelligence (AAAI-90), Technical Program Chair of the Neural Information Processing Systems (NIPS-2000) and General Chair of NIPS-2001 He is a Fellow of the ACM, AAAI, and AAAS. He served as founding President of the International Machine Learning Society, and he is currently a member of the Steering Committee of the Asian Conference on Machine Learning.

Research group:

AI - Machine Learning - Algorithms

Research Interests:

Research Description

I am interested in all aspects of machine learning. There are three major strands of my research. First, I am interested in the fundamental questions of artificial intelligence and how machine learning can provide the basis for building integrated intelligent systems. This includes learning for sequential decision making, particularly hierarchical reinforcement learning, and understanding how intelligent systems can detect anomalies and manage both the “known unknowns” and the “unknown unknowns” of the worlds in which they operate.

Second, I am interested in ways that people and computers can collaborate to solve challenging problems. How can we create rich interactions between people and computers so that learning can occur very quickly and easily? How can machine learning system learn “in the wild” without an engineer intervening to adjust parameters or change features and where the user feedback may be very noisy and indirect? How can we develop and refine the practice of software engineering of adaptive systems, so that BSCS engineers can build effective learning systems? How can an AI system recognize and understand the goals and actions of the user so that it can provide useful assistance?

Third, I am interested in applying machine learning to problems in the ecological sciences and ecosystem management as part of the emerging field of Computational Sustainability. This includes data cleaning and anomaly detection for sensor data, automated insect recognition for biodiversity surveys, computer vision for recognizing and understanding animal behavior, machine learning models of species distributions and migrations, and methods for solving large-scale ecosystem management problems. A related topic is the application of machine learning to model and control office buildings, such as the Kelley Engineering Center.

Publications

In Press

Dereszynski, E. W., and T. G. Dietterich, "Spatiotemporal Models for Data-Anomaly Detection in Dynamic Environmental Monitoring Campaigns", ACM Transactions on Sensor Networks, In Press. Abstract

2011

Lin, J., N. Larios, D. A. Lytle, A. Moldenke, R. Paasch, L. G. Shapiro, S. Todorovic, and T. G. Dietterich, "Fine-Grained Recognition for Arthropod Field Surveys: Three Image Collections", First Workshop on Fine-Grained Visual Categorization (CVPR-2011), 2011. Abstract

Dereszynski, E. W., J. Hostetler, A. Fern, T. G. Dietterich, T. - T. Hoang, and M. Udarbe, "Learning Probabilistic Behavior Models in Real-time Strategy Games", Artificial Intelligence in Digital Entertainment (AIIDE-2011), 2011. Abstract

Hutchinson, R. A., L. - P. Liu, and T. G. Dietterich, "Incorporating Boosted Regression Trees into Ecological Latent Variable Models", AAAI, San Francisco, California, 08/2011. Abstract

Sorower, M. S., T. G. Dietterich, J. R. Doppa, P. Tadepalli, and X. Fern, "Learning Rules from Incomplete Examples via a Probabilistic Mention Model", IJCAI 2011 Workshop on Learning by Reading and its Applications in Intelligent Question-Answering, Barcelona, Catalonia, Spain, 07/2011. Abstract

Doppa, J. R., M. NasrEsfahani, M. S. Sorower, J. Irvine, T. G. Dietterich, X. Fern, and P. Tadepalli, "Learning Rules from Incomplete Examples via Observation Models", IJCAI 2011 Workshop on Learning by Reading and its Applications in Intelligent Question-Answering, Barcelona, Catalonia, Spain, 07/2011. Abstract

Bao, X., and T. G. Dietterich, "FolderPredictor: Reducing the Cost of Reaching the Right Folder", ACM Transactions on Intelligent Systems and Technology, vol. 2, issue 1, 01/2011. Abstract

Larios, N., J. Lin, M. Zhang, D. A. Lytle, A. Moldenke, L. G. Shapiro, and T. G. Dietterich, "Stacked Spatial-Pyramid Kernel: An Object-Class Recognition Method to Combine Scores from Random Trees", IEEE Workshop on Applications of Computer Vision, Kona, HI, pp. 329 - 335, 01/2011. Abstract

2010

Barford, P., P. Dacier, T. G. Dietterich, M. Fredrikson, J. Giffin, S. Jajodia, S. Jha, J. Li, P. Liu, P. Ning, et al., "Cyber SA: Situational Awareness for Cyber Defense", Cyber Situational Awareness: Springer, pp. 3-13, 2010. Abstract

Dietterich, T. G., X. Bao, V. Keiser, and J. Shen, "Machine Learning Methods for High Level Cyber Situation Awareness", Cyber Situational Awareness: Springer, pp. 227-247, 2010.

Larios, N., B. Soran, L. G. Shapiro, G. Martínez-Muñoz, J. Lin, and T. G. Dietterich, "Haar Random Forest Features and SVM Spatial Matching Kernel for Stonefly Species Identification", 2010 20th International Conference on Pattern Recognition (ICPR)2010 20th International Conference on Pattern Recognition, Istanbul, Turkey, IEEE, pp. 2624 - 2627, 10/2010. Abstract

Lytle, D. A., G. Martínez-Muñoz, W. Zhang, N. Larios, L. G. Shapiro, R. Paasch, A. Moldenke, E. N. Mortensen, S. Todorovic, and T. G. Dietterich, "Automated processing and identification of benthic invertebrate samples", Journal of the North American Benthological Society, vol. 29, issue 3, pp. 867 - 874, 09/2010. Abstract

Judah, K., S. Roy, A. Fern, and T. G. Dietterich, "Reinforcement Learning via Practice and Critique Advice", AAAI Conference on Artificial Intelligence (AAAI-10), Atlanta, GA, 07/2010. Abstract

Jensen, C., H. Lonsdale, E. Wynn, J. Cao, M. Slater, and T. G. Dietterich, "The life and times of files and information: a study of desktop provenance", Proceedings of the 28th international conference on Human factors in computing systems - CHI '10, Atlanta, Georgia, ACM Press, pp. 767-776, 04/2010. Abstract

2009

Shen, J., and T. G. Dietterich, "A family of large margin linear classifiers and its application in dynamic environments", Statistical Analysis and Data Mining, vol. 2, issue 5-6, pp. 328 - 345, 12/2009. Abstract

Zhang, X., S. Yoon, P. DiBona, D. S. Appling, L. Ding, J. R. Doppa, D. Green, J. K. Guo, U. Kuter, G. Levine, et al., "An Ensemble Learning and Problem-Solving Architecture for Airspace Management", Proceedings of Twenty-First Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-09), Pasadena, CA, pp. 203-210, 07/2009. Abstract

Keiser, V., and T. G. Dietterich, "Evaluating Online Text Classification Algorithms for Email Prediction in TaskTracer", Conference on Email and Anti-Spam, Mountain View, CA, 07/2009. Abstract

Dietterich, T. G., "Machine learning in ecosystem informatics and sustainability", Proceedings of the 21st International Joint Conference on Artifical Intelligence, Pasadena, CA, Morgan Kaufmann Publishers Inc., pp. 8–13, 07/2009. Abstract

Dietterich, T. G., A. Fern, J. Irvine, M. Slater, P. Tadepalli, M. Gervasio, C. Ellwood, B. Jarrold, O. Brdiczka, and J. Blythe, "User Initiated Learning for Adaptive Interfaces Abstract", IJCAI2009 Workshop on Intelligence and Interaction, Pasadena, CA, 07/2009. Abstract

Martinez-Munoz, G., N. Larios, E. N. Mortensen, W. Zhang, A. Yamamuro, R. Paasch, N. Payet, D. A. Lytle, L. G. Shapiro, S. Todorovic, et al., "Dictionary-free categorization of very similar objects via stacked evidence trees", 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), Miami, FL, IEEE, pp. 549 - 556, 06/2009. Abstract

Stumpf, S., V. Rajaram, L. Li, W. - K. Wong, M. M. Burnett, T. G. Dietterich, E. Sullivan, and J. L. Herlocker, "Interacting meaningfully with machine learning systems: Three experiments", International Journal of Human-Computer Studies, vol. 67, issue 8, pp. 639 - 662, 06/2009. Abstract

Zhang, W., A. Surve, X. Fern, and T. G. Dietterich, "Learning non-redundant codebooks for classifying complex objects", Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09, Montreal, Quebec, Canada, ACM Press, pp. 1 - 8, 06/2009. Abstract

Shen, J., J. Irvine, X. Bao, M. Goodman, S. Kolibaba, A. Tran, F. Carl, B. Kirschner, S. Stumpf, and T. G. Dietterich, "Detecting and correcting user activity switches", Proceedingsc of the 13th International Conference on Intelligent User Interfaces - IUI '09, Sanibel Island, Florida, USA, ACM Press, pp. 117, 02/2009. Abstract

Shen, J., E. Fitzhenry, and T. G. Dietterich, "Discovering frequent work procedures from resource connections", Proceedings of the 13th International Conference on Intelligent User Interfaces - IUI '09, Sanibel Island, Florida, USA, ACM Press, pp. 277-286, 02/2009. Abstract

2008

Larios, N., H. Deng, W. Zhang, M. Sarpola, J. Yuen, R. Paasch, A. Moldenke, D. A. Lytle, S. R. Correa, E. N. Mortensen, et al., "Automated insect identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects", Machine Vision and Applications, vol. 19, issue 2, pp. 105 - 123, 3/2008. Abstract

School of Electrical Engineering & Computer Science