Thomas Dietterich

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Prasad TadepalliProfessor of Computer Science, Oregon State UniversityVerified email at eecs.oregonstate.edu
Dan HendrycksDirector of the Center for AI SafetyVerified email at berkeley.edu
Jon HerlockerCEO Tignis, incVerified email at herlocker.org
David G. UllmanDavid Ullman LLC.Verified email at davidullman.com
Linda ShapiroProfessor of Computer Science & Engineering, University of WashingtonVerified email at cs.washington.edu
Richard H. LathropProfessor of Computer Science, University of California IrvineVerified email at uci.edu
Hussein AlmuallimPresident, FirmSoft TechnologiesVerified email at firmsofttech.com
Weng-Keen WongProfessor of Computer Science, Oregon State UniversityVerified email at eecs.oregonstate.edu
Tomas Lozano-PerezProfessor of Computer Science, MITVerified email at mit.edu
Nicholas S. FlannAssociate Professor of Computer Science, Utah State UniversityVerified email at usu.edu
Jude ShavlikProfessor of Computer Science and of Biostatistics & Medical Informatics, University of WisconsinVerified email at cs.wisc.edu
David A. LytleOregon State UniversityVerified email at oregonstate.edu
Simone StumpfUniversity of GlasgowVerified email at glasgow.ac.uk
Dragos D MargineantuBoeing Research & TechnologyVerified email at boeing.com
Jianqiang ShenPalo Alto Research Center (PARC)Verified email at parc.com
Xiaoli FernOregon State UniversityVerified email at eecs.oregonstate.edu
Janardhan Rao (Jana) DoppaHuie-Rogers Endowed Chair Associate Professor, Washington State UniversityVerified email at eecs.wsu.edu
Hongli Dengprincipal applied scientistVerified email at amazon.com
Claire MontgomeryEmeritus Professor, Oregon State University, College of ForestryVerified email at oregonstate.edu
Daniel SheldonUniversity of Massachusetts AmherstVerified email at cs.umass.edu

Thomas Dietterich

Distinguished Professor (Emeritus), Computer Science, Oregon State University

Verified email at cs.orst.edu - Homepage

Machine Learning Computational Sustainability Artificial Intelligence Reinforcement Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Ensemble methods in machine learning TG Dietterich International workshop on multiple classifier systems, 1-15, 2000	10911	2000
Approximate statistical tests for comparing supervised classification learning algorithms TG Dietterich Neural computation 10 (7), 1895-1923, 1998	4643	1998
Solving multiclass learning problems via error-correcting output codes TG Dietterich, G Bakiri Journal of artificial intelligence research 2, 263-286, 1994	3929	1994
An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization TG Dietterich Machine learning 40, 139-157, 2000	3921	2000
Benchmarking neural network robustness to common corruptions and perturbations D Hendrycks, T Dietterich arXiv preprint arXiv:1903.12261, 2019	3566	2019
Solving the multiple instance problem with axis-parallel rectangles TG Dietterich, RH Lathrop, T Lozano-Pérez Artificial intelligence 89 (1-2), 31-71, 1997	3563	1997
Machine-learning research: Four Current Directions TG Dietterich AI magazine 18 (4), 97, 1997	2164	1997
Hierarchical reinforcement learning with the MAXQ value function decomposition TG Dietterich Journal of artificial intelligence research 13, 227-303, 2000	2161	2000
Deep anomaly detection with outlier exposure D Hendrycks, M Mazeika, T Dietterich arXiv preprint arXiv:1812.04606, 2018	1640	2018
Ensemble learning TG Dietterich The handbook of brain theory and neural networks 2 (1), 110-125, 2002	1296*	2002
Learning with many irrelevant features H Almuallim, TG Dietterich Oregon State University, 1991	1082	1991
Overfitting and undercomputing in machine learning T Dietterich ACM computing surveys (CSUR) 27 (3), 326-327, 1995	1072	1995
The eBird enterprise: An integrated approach to development and application of citizen science BL Sullivan, JL Aycrigg, JH Barry, RE Bonney, N Bruns, CB Cooper, ... Biological conservation 169, 31-40, 2014	1069	2014
Machine learning for sequential data: A review TG Dietterich Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR …, 2002	1002	2002
A unifying review of deep and shallow anomaly detection L Ruff, JR Kauffmann, RA Vandermeulen, G Montavon, W Samek, M Kloft, ... Proceedings of the IEEE 109 (5), 756-795, 2021	954	2021
Pruning adaptive boosting DD Margineantu, TG Dietterich ICML 97, 211-218, 1997	817	1997
To transfer or not to transfer MT Rosenstein, Z Marx, LP Kaelbling, TG Dietterich NIPS 2005 workshop on transfer learning 898 (3), 4, 2005	743	2005
Learning boolean concepts in the presence of many irrelevant features H Almuallim, TG Dietterich Artificial intelligence 69 (1-2), 279-305, 1994	718	1994
A reinforcement learning approach to job-shop scheduling W Zhang, TG Dietterich Ijcai 95, 1114-1120, 1995	638	1995
Readings in machine learning J Shavlik, T Dietterich Morgan Kaufmann Publishers., 1990	634	1990

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