Jacob Abernethy

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Peter BartlettProfessor, EECS and Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Theodoros EvgeniouINSEADVerified email at insead.edu
Jean-Philippe VertGoogle Brain / Mines ParisTechVerified email at mines-paristech.fr
Ambuj Tewari | अम्बुज तिवारीAssociate Professor, Department of Statistics and Department of EECS, University of MichiganVerified email at umich.edu
Elad HazanProfessor at Princeton University and Director Google AI PrincetonVerified email at cs.princeton.edu
Rafael FrongilloComputer Science, CU BoulderVerified email at cs.berkeley.edu
Olivier ChapelleGoogleVerified email at chapelle.cc
Carlos CastilloDistinguished Research Professor, Universitat Pompeu FabraVerified email at upf.edu
Jennifer Wortman VaughanSenior Principal Researcher, Microsoft Research, New York CityVerified email at microsoft.com
Francis BachInria - Ecole Normale SupérieureVerified email at inria.fr
Yiling ChenHarvard UniversityVerified email at seas.harvard.edu
John LangfordMicrosoft Research New YorkVerified email at hunch.net
Alekh AgarwalMicrosoft ResearchVerified email at microsoft.com
Chien-Ju HoWashington University in St. LouisVerified email at wustl.edu
Satyen KaleStaff Research Scientist, Google ResearchVerified email at google.com
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 @ Nvidia ResearchVerified 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	324*	2017
Competing in the dark: An efficient algorithm for bandit linear optimization JD Abernethy, E Hazan, A Rakhlin	313	2009
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	287	2009
Optimal strategies and minimax lower bounds for online convex games J Abernethy, PL Bartlett, A Rakhlin, A Tewari	155	2008
Optimal strategies and minimax lower bounds for online convex games J Abernethy, PL Bartlett, A Rakhlin, A Tewari	155	2008
Low-rank matrix factorization with attributes J Abernethy, F Bach, T Evgeniou, JP Vert arXiv preprint cs/0611124, 2006	107	2006
Web spam identification through content and hyperlinks J Abernethy, O Chapelle, C Castillo Proceedings of the 4th international workshop on Adversarial information …, 2008	105	2008
A stochastic view of optimal regret through minimax duality J Abernethy, A Agarwal, PL Bartlett, A Rakhlin arXiv preprint arXiv:0903.5328, 2009	104	2009
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	86	2011
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	85	2013
Graph regularization methods for web spam detection J Abernethy, O Chapelle, C Castillo Machine Learning 81 (2), 207-225, 2010	74	2010
A collaborative mechanism for crowdsourcing prediction problems J Abernethy, RM Frongillo arXiv preprint arXiv:1111.2664, 2011	66	2011
Multitask learning with expert advice J Abernethy, P Bartlett, A Rakhlin International Conference on Computational Learning Theory, 484-498, 2007	65	2007
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	61	2007
Dynamic online pricing with incomplete information using multiarmed bandit experiments K Misra, EM Schwartz, J Abernethy Marketing Science 38 (2), 226-252, 2019	58	2019
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	55	2012
Beating the adaptive bandit with high probability J Abernethy, A Rakhlin 2009 Information Theory and Applications Workshop, 280-289, 2009	53	2009
Online linear optimization via smoothing J Abernethy, C Lee, A Sinha, A Tewari Conference on Learning Theory, 807-823, 2014	52	2014
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	52	2011
WITCH: A new approach to Web spam detection J Abernethy, O Chapelle, C Castillo In Proceedings of the 4th International Workshop on Adversarial Information …, 2008	48	2008

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