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Kevin Leyton-Brown
Kevin Leyton-Brown
Professor, Computer Science, University of British Columbia; Canada CIFAR AI Chair
Zweryfikowany adres z cs.ubc.ca - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Multiagent systems: Algorithmic, game-theoretic, and logical foundations
Y Shoham, K Leyton-Brown
Cambridge University Press, 2009
35432009
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
29292011
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
1867*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
12072009
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
11002008
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
8612001
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
8602017
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
8182016
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
6601999
Algorithm runtime prediction: Methods & evaluation
F Hutter, L Xu, HH Hoos, K Leyton-Brown
Artificial Intelligence 206, 79-111, 2014
5452014
An efficient approach for assessing hyperparameter importance
F Hutter, H Hoos, K Leyton-Brown
International Conference on Machine Learning, 2014
5142014
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
5072000
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
4322013
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
3002017
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
2712004
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
2502002
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
2472004
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
2422014
Counterfactual prediction with deep instrumental variables networks
J Hartford, G Lewis, K Leyton-Brown, M Taddy
arXiv preprint arXiv:1612.09596, 2016
2392016
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