Kevin Leyton-Brown

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Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaVerified email at cs.rwth-aachen.de
Frank HutterProfessor of Computer Science, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
Yoav ShohamProfessor (emeritus)Verified email at cs.stanford.edu
Lin XuUniversity of British ColumbiaVerified email at cs.ubc.ca
Albert X. JiangAssistant Professor of Computer Science, Trinity UniversityVerified email at trinity.edu
James R. WrightUniversity of AlbertaVerified email at ualberta.ca
Jason HartfordSenior Machine Learning Research Scientist at Recursion PharmaVerified email at cs.ubc.ca
Milind TambeProfessor & Director CRCS Center @Harvard; Director "AI for Social Good" @Google ResearchVerified email at harvard.edu
Moshe TennenholtzVerified email at ie.technion.ac.il
David R. M. ThompsonGoogleVerified email at google.com
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence LUH|AI, L3S ResearchVerified email at ai.uni-hannover.de
Ece KamarMicrosoft ResearchVerified email at microsoft.com
Ryan CaloUniversity of WashingtonVerified email at uw.edu
rodney brooksProfessor of Computer Science, MITVerified email at csail.mit.edu
David C. ParkesGeorge F. Colony Professor of Computer Science, Harvard UniversityVerified email at eecs.harvard.edu
Oren EtzioniCEO, Allen Institute for AI (AI2)Verified email at allenai.org
Julia HirschbergColumbia UniversityVerified email at cs.columbia.edu
Thomas StützleF.R.S.-FNRS Research Director, Université Libre de Bruxelles (ULB)Verified email at ulb.ac.be
Neil NewmanUniversity of British ColumbiaVerified email at cs.ubc.ca

Kevin Leyton-Brown

Professor, Computer Science, University of British Columbia; Canada CIFAR AI Chair

Verified email at cs.ubc.ca - Homepage

artificial intelligence machine learning game theory algorithms market design


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Multiagent systems: Algorithmic, game-theoretic, and logical foundations Y Shoham, K Leyton-Brown Cambridge University Press, 2009	3544	2009
Sequential model-based optimization for general algorithm configuration F Hutter, HH Hoos, K Leyton-Brown Proceedings of the 5th International Conference on Learning and Intelligent …, 2011	2931	2011
Auto-WEKA: Automated selection and hyper-parameter optimization of classification algorithms C Thornton, F Hutter, HH Hoos, K Leyton-Brown CoRR, abs/1208.3719, 2012	1869*	2012
ParamILS: an automatic algorithm configuration framework F Hutter, HH Hoos, K Leyton-Brown, T Stützle Journal of Artificial Intelligence Research (JAIR) 36 (1), 267-306, 2009	1207	2009
SATzilla: Portfolio-based Algorithm Selection for SAT L Xu, F Hutter, HH Hoos, K Leyton-Brown Journal of Artificial Intelligence Research (JAIR) 32, 565-606, 2008	1100	2008
Incentives for sharing in peer-to-peer networks P Golle, K Leyton-Brown, I Mironov, M Lillibridge ACM Conference on Electronic Commerce, 75-87, 2001	861	2001
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA L Kotthoff, C Thornton, HH Hoos, F Hutter, K Leyton-Brown Journal of Machine Learning Research 18 (25), 1--5, 2017	860	2017
Artificial intelligence and life in 2030: the one hundred year study on artificial intelligence P Stone, R Brooks, E Brynjolfsson, R Calo, O Etzioni, G Hager, ... arXiv preprint arXiv:2211.06318, 2016	821	2016
Essentials of Game Theory: A Concise Multidisciplinary Introduction K Leyton-Brown, Y Shoham Synthesis Lectures on Artificial Intelligence and Machine Learning, 1-88, 2008	774*	2008
Taming the computational complexity of combinatorial auctions: Optimal and approximate approaches Y Fujishima, K Leyton-Brown, Y Shoham International Joint Conference on Artificial Intelligence (IJCAI) 99, 548-553, 1999	660	1999
Algorithm runtime prediction: Methods & evaluation F Hutter, L Xu, HH Hoos, K Leyton-Brown Artificial Intelligence 206, 79-111, 2014	545	2014
An efficient approach for assessing hyperparameter importance F Hutter, H Hoos, K Leyton-Brown International Conference on Machine Learning, 2014	514	2014
Towards a universal test suite for combinatorial auction algorithms K Leyton-Brown, M Pearson, Y Shoham Proceedings of the 2nd ACM conference on Electronic Commerce, 66-76, 2000	507	2000
Towards an empirical foundation for assessing Bayesian optimization of hyperparameters K Eggensperger, M Feurer, F Hutter, J Bergstra, J Snoek, H Hoos, ... NIPS Workshop on Bayesian Optimization in Theory and Practice, 2013	432	2013
Deep IV: A flexible approach for counterfactual prediction J Hartford, G Lewis, K Leyton-Brown, M Taddy International Conference on Machine Learning, 1414-1423, 2017	302	2017
Understanding random SAT: Beyond the clauses-to-variables ratio E Nudelman, K Leyton-Brown, H Hoos, A Devkar, Y Shoham Principles and Practice of Constraint Programming, 438-452, 2004	272	2004
Learning the empirical hardness of optimization problems: The case of combinatorial auctions K Leyton-Brown, E Nudelman, Y Shoham Principles and Practice of Constraint Programming-CP 2002: 8th International …, 2002	250	2002
Run the GAMUT: A comprehensive approach to evaluating game-theoretic algorithms E Nudelman, J Wortman, Y Shoham, K Leyton-Brown Proceedings of the Third International Joint Conference on Autonomous Agents …, 2004	247	2004
ASlib: A benchmark library for algorithm selection B Bischl, P Kerschke, L Kotthoff, M Lindauer, Y Malitsky, A Fréchette, ... Artificial Intelligence 237, 41–58, 2014	242	2014
A portfolio approach to algorithm selection K Leyton-Brown, E Nudelman, G Andrew, J McFadden, Y Shoham International Joint Conference on Artificial Intelligence (IJCAI) 1543, 2003, 2003	239	2003

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