Jacob Abernethy
Jacob Abernethy
Assistant Professor, University of Michigan
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Cited by
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On convergence and stability of gans
N Kodali, J Abernethy, J Hays, Z Kira
arXiv preprint arXiv:1705.07215, 2017
Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization.
JD Abernethy, E Hazan, A Rakhlin
COLT, 263-274, 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
Dynamic online pricing with incomplete information using multiarmed bandit experiments
K Misra, EM Schwartz, J Abernethy
Marketing Science 38 (2), 226-252, 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
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
Low-rank matrix factorization with attributes
J Abernethy, F Bach, T Evgeniou, JP Vert
arXiv preprint cs/0611124, 2006
Web spam identification through content and hyperlinks
J Abernethy, O Chapelle, C Castillo
Proceedings of the 4th international workshop on Adversarial information …, 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
A stochastic view of optimal regret through minimax duality
J Abernethy, A Agarwal, PL Bartlett, A Rakhlin
arXiv preprint arXiv:0903.5328, 2009
Graph regularization methods for web spam detection
J Abernethy, O Chapelle, C Castillo
Machine Learning 81, 207-225, 2010
Online linear optimization via smoothing
J Abernethy, C Lee, A Sinha, A Tewari
Conference on learning theory, 807-823, 2014
Fighting bandits with a new kind of smoothness
JD Abernethy, C Lee, A Tewari
Advances in Neural Information Processing Systems 28, 2015
A collaborative mechanism for crowdsourcing prediction problems
JD Abernethy, R Frongillo
Advances in neural information processing systems 24, 2011
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
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
Beating the adaptive bandit with high probability
J Abernethy, A Rakhlin
2009 Information Theory and Applications Workshop, 280-289, 2009
A characterization of scoring rules for linear properties
JD Abernethy, RM Frongillo
Conference on Learning Theory, 27.1-27.13, 2012
Multitask learning with expert advice
J Abernethy, P Bartlett, A Rakhlin
Learning Theory: 20th Annual Conference on Learning Theory, COLT 2007, San …, 2007
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
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