Jacob Abernethy

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Peter BartlettProfessor, EECS and Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganVerified email at umich.edu
Elad HazanProfessor at Princeton University and Director Google AI PrincetonVerified email at princeton.edu
Theodoros EvgeniouINSEADVerified email at insead.edu
Jean-Philippe VertOwkin / PSL UniversityVerified email at owkin.com
Rafael FrongilloComputer Science, CU BoulderVerified email at colorado.edu
Olivier ChapelleGoogleVerified email at chapelle.cc
Jennifer Wortman VaughanSenior Principal Researcher, Microsoft Research, New York CityVerified email at microsoft.com
Carlos CastilloICREA Research Professor at Universitat Pompeu FabraVerified email at icrea.cat
Francis BachInria - Ecole Normale SupérieureVerified email at inria.fr
Yiling ChenHarvard UniversityVerified email at seas.harvard.edu
Alekh AgarwalGoogleVerified email at google.com
John LangfordMicrosoft Research New YorkVerified email at hunch.net
Satyen KaleResearch Scientist, AppleVerified email at satyenkale.com
Chien-Ju HoWashington University in St. LouisVerified email at wustl.edu
niv buchbinderTel Aviv universityVerified email at post.tau.ac.il
Isabelle StantonSoftware Engineer at GoogleVerified email at eecs.berkeley.edu
Michael KearnsProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Qingsi WangGoogleVerified email at google.com
Shie MannorProfessor of Electrical Engineering @ Technion & Researcher @ NvidiaVerified email at technion.ac.il

Jacob Abernethy

Assistant Professor, University of Michigan

Verified email at umich.edu

Machine Learning Game Theory Sequential Decision Making


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On convergence and stability of gans N Kodali, J Abernethy, J Hays, Z Kira arXiv preprint arXiv:1705.07215, 2017	848*	2017
Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization. JD Abernethy, E Hazan, A Rakhlin COLT, 263-274, 2008	419	2008
A new approach to collaborative filtering: Operator estimation with spectral regularization. J Abernethy, F Bach, T Evgeniou, JP Vert Journal of Machine Learning Research 10 (3), 2009	314	2009
Dynamic online pricing with incomplete information using multiarmed bandit experiments K Misra, EM Schwartz, J Abernethy Marketing Science 38 (2), 226-252, 2019	240	2019
Optimal strategies and minimax lower bounds for online convex games J Abernethy, PL Bartlett, A Rakhlin, A Tewari Proceedings of the 21st annual conference on learning theory, 414-424, 2008	205	2008
Blackwell approachability and no-regret learning are equivalent J Abernethy, PL Bartlett, E Hazan Proceedings of the 24th Annual Conference on Learning Theory, 27-46, 2011	142	2011
Low-rank matrix factorization with attributes J Abernethy, F Bach, T Evgeniou, JP Vert arXiv preprint cs/0611124, 2006	109	2006
Web spam identification through content and hyperlinks J Abernethy, O Chapelle, C Castillo Proceedings of the 4th international workshop on Adversarial information …, 2008	108	2008
Efficient market making via convex optimization, and a connection to online learning J Abernethy, Y Chen, JW Vaughan ACM Transactions on Economics and Computation (TEAC) 1 (2), 1-39, 2013	106	2013
A stochastic view of optimal regret through minimax duality J Abernethy, A Agarwal, PL Bartlett, A Rakhlin arXiv preprint arXiv:0903.5328, 2009	105	2009
Graph regularization methods for web spam detection J Abernethy, O Chapelle, C Castillo Machine Learning 81, 207-225, 2010	88	2010
Fighting bandits with a new kind of smoothness JD Abernethy, C Lee, A Tewari Advances in Neural Information Processing Systems 28, 2015	86	2015
Online linear optimization via smoothing J Abernethy, C Lee, A Sinha, A Tewari Conference on learning theory, 807-823, 2014	83	2014
A collaborative mechanism for crowdsourcing prediction problems JD Abernethy, R Frongillo Advances in neural information processing systems 24, 2011	81	2011
Interior-point methods for full-information and bandit online learning JD Abernethy, E Hazan, A Rakhlin IEEE Transactions on Information Theory 58 (7), 4164-4175, 2012	76	2012
Eliciting consumer preferences using robust adaptive choice questionnaires J Abernethy, T Evgeniou, O Toubia, JP Vert IEEE Transactions on Knowledge and Data Engineering 20 (2), 145-155, 2007	76	2007
Beating the adaptive bandit with high probability J Abernethy, A Rakhlin 2009 Information Theory and Applications Workshop, 280-289, 2009	72	2009
A characterization of scoring rules for linear properties JD Abernethy, RM Frongillo Conference on Learning Theory, 27.1-27.13, 2012	71	2012
An optimization-based framework for automated market-making J Abernethy, Y Chen, J Wortman Vaughan Proceedings of the 12th ACM conference on Electronic commerce, 297-306, 2011	65	2011
Multitask learning with expert advice J Abernethy, P Bartlett, A Rakhlin Learning Theory: 20th Annual Conference on Learning Theory, COLT 2007, San …, 2007	65	2007

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