T
Thomas G. Dietterich
Researcher at Oregon State University
Publications - 286
Citations - 58937
Thomas G. Dietterich is an academic researcher from Oregon State University. The author has contributed to research in topics: Reinforcement learning & Markov decision process. The author has an hindex of 74, co-authored 279 publications receiving 51935 citations. Previous affiliations of Thomas G. Dietterich include University of Wyoming & Stanford University.
Papers
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Dissertation
Methods for cost-sensitive learning
TL;DR: B-LOTs were shown to be superior to other methods in cases where the classes have very different frequencies—a situation that arises frequently in cost-sensitive classification problems.
Journal ArticleDOI
Next-generation phenomics for the Tree of Life
J. Gordon Burleigh,Kenzley Alphonse,Andrew J. Alverson,Holly M. Bik,Carrine E. Blank,Andrea L. Cirranello,Hong Cui,Marymegan Daly,Thomas G. Dietterich,Gail E. Gasparich,Jed Irvine,Matthew L. Julius,Seth Kaufman,Edith Law,Jing Liu,Lisa R. Moore,Maureen A. O'Leary,Maria Passarotti,Sonali Ranade,Nancy B. Simmons,Dennis W. Stevenson,Robert W. Thacker,Edward C. Theriot,Sinisa Todorovic,Paúl M. Velazco,Ramona Walls,Joanna M. Wolfe,Mengjie Yu +27 more
TL;DR: The goal is to assemble large phenomic datasets built using new methods and to provide the public and scientific community with tools for phenomic data assembly that will enable rapid and automated study of phenotypes across the Tree of Life.
Journal ArticleDOI
Letter to the Editor: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter
Stuart Russell,Thomas G. Dietterich,Eric Horvitz,Bart Selman,Francesca Rossi,Demis Hassabis,Shane Legg,Mustafa Suleyman,Dileep George,D. Scott Phoenix +9 more
TL;DR: It is believed that research on how to make AI systems robust and beneficial is both important and timely, and that there are concrete research directions that can be pursued today.
Journal ArticleDOI
Sequential Feature Explanations for Anomaly Detection
TL;DR: In this paper, the authors study the problem of computing and evaluating sequential feature explanations (SFEs) for anomaly detectors and present both greedy algorithms and an optimal algorithm, based on branch-and-bound search, for optimizing SFEs.
Proceedings Article
Real-time detection of task switches of desktop users
TL;DR: This paper presents a framework that analyzes a sequence of observations to detect task switches and studies three combination methods: simple voting, a likelihood ratio test that assesses the variability of the task probabilities over the sequence of windows, and application of the Viterbi algorithm under an assumed task transition cost model.