Kevin Leyton-Brown - Google Scholar

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	All	Since 2015
Citations	15694	8905
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i10-index	103	83

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Co-authors

Holger HoosLeiden University, The Netherlands & University of British Columbia, CanadaVerified email at liacs.nl
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
Moshe TennenholtzVerified email at ie.technion.ac.il
David R. M. ThompsonGoogleVerified email at google.com
Milind TambeDirector CRCS Center @Harvard; Director "AI for Social Good" @Google India ResearchVerified email at harvard.edu
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Jason HartfordPhD Student, University of British ColumbiaVerified email at cs.ubc.ca
Ilya MironovGoogleVerified email at google.com
Marius LindauerLeibniz University Hannover (Germany)Verified email at tnt.uni-hannover.de
Alexandre FréchettePhD Student, University of British ColumbiaVerified email at cs.ubc.ca
Thomas StützleF.R.S.-FNRS Research Director, Université Libre de Bruxelles (ULB)Verified email at ulb.ac.be
Katharina EggenspergerPhD Student, Machine Learning Lab, University of FreiburgVerified email at informatik.uni-freiburg.de
Mark LillibridgeFacebookVerified email at cs.cmu.edu
Anne CondonProfessor of Computer Science, University of British ColumbiaVerified email at cs.ubc.ca
Tuomas SandholmAngel Jordan University Professor of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Zhengyu YinComputer Science USCVerified email at usc.edu

Kevin Leyton-Brown

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

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	2572	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	1418	2011
Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms C Thornton, F Hutter, HH Hoos, K Leyton-Brown Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013	899	2013
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	842	2009
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	796	2001
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	793	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	632	1999
Essentials of Game Theory: A Concise Multidisciplinary Introduction K Leyton-Brown, Y Shoham Synthesis Lectures on Artificial Intelligence and Machine Learning, 1-88, 2008	524	2008
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	438	2000
Algorithm runtime prediction: Methods & evaluation F Hutter, L Xu, HH Hoos, K Leyton-Brown Artificial Intelligence 206, 79-111, 2014	324	2014
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA L Kotthoff, C Thornton, HH Hoos, F Hutter, K Leyton-Brown The Journal of Machine Learning Research 18 (1), 826-830, 2017	311	2017
Learning the empirical hardness of optimization problems: The case of combinatorial auctions K Leyton-Brown, E Nudelman, Y Shoham International conference on principles and practice of constraint …, 2002	217	2002
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	210	2004
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	207	2004
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	202	2013
Performance prediction and automated tuning of randomized and parametric algorithms F Hutter, Y Hamadi, HH Hoos, K Leyton-Brown Principles and Practice of Constraint Programming, 213-228, 2006	190	2006
An algorithm for multi-unit combinatorial auctions K Leyton-Brown, Y Shoham, M Tennenholtz National Conference on Artificial Intelligence (AAAI), 56-61, 2000	188	2000
Deep optimization for spectrum repacking N Newman, A Fréchette, K Leyton-Brown Communications of the ACM 61 (1), 97-104, 2017	184*	2017
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	179	2003
An efficient approach for assessing hyperparameter importance F Hutter, H Hoos, K Leyton-Brown International Conference on Machine Learning, 2014	160	2014

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