Abernethy, Jacob D

34 publications

AISTATS 2024 Extragradient Type Methods for Riemannian Variational Inequality Problems Zihao Hu, Guanghui Wang, Xi Wang, Andre Wibisono, Jacob D Abernethy, Molei Tao

NeurIPS 2023 Faster Margin Maximization Rates for Generic Optimization Methods Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy

COLT 2023 Minimizing Dynamic Regret on Geodesic Metric Spaces Zihao Hu, Guanghui Wang, Jacob D. Abernethy

NeurIPS 2023 Riemannian Projection-Free Online Learning Zihao Hu, Guanghui Wang, Jacob D. Abernethy

ICML 2022 Active Sampling for Min-Max Fairness Jacob D Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang

ICML 2022 ActiveHedge: Hedge Meets Active Learning Bhuvesh Kumar, Jacob D Abernethy, Venkatesh Saligrama

NeurIPS 2022 Adaptive Oracle-Efficient Online Learning Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy

ICML 2021 A Modular Analysis of Provable Acceleration via Polyak’s Momentum: Training a Wide ReLU Network and a Deep Linear Network Jun-Kun Wang, Chi-Heng Lin, Jacob D Abernethy

NeurIPS 2021 Observation-Free Attacks on Stochastic Bandits Yinglun Xu, Bhuvesh Kumar, Jacob D. Abernethy

NeurIPS 2019 Learning Auctions with Robust Incentive Guarantees Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie H Morgenstern

NeurIPS 2019 Online Learning via the Differential Privacy Lens Jacob D. Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari

NeurIPS 2018 Acceleration Through Optimistic No-Regret Dynamics Jun-Kun Wang, Jacob D. Abernethy

COLT 2018 Faster Rates for Convex-Concave Games Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang

NeurIPS 2017 On Frank-Wolfe and Equilibrium Computation Jacob D. Abernethy, Jun-Kun Wang

NeurIPS 2016 Threshold Bandits, with and Without Censored Feedback Jacob D. Abernethy, Kareem Amin, Ruihao Zhu

NeurIPS 2015 A Market Framework for Eliciting Private Data Bo Waggoner, Rafael Frongillo, Jacob D. Abernethy

NeurIPS 2015 Fighting Bandits with a New Kind of Smoothness Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari

COLT 2014 Online Linear Optimization via Smoothing Jacob D. Abernethy, Chansoo Lee, Abhinav Sinha, Ambuj Tewari

COLT 2012 A Characterization of Scoring Rules for Linear Properties Jacob D. Abernethy, Rafael M. Frongillo

NeurIPS 2011 A Collaborative Mechanism for Crowdsourcing Prediction Problems Jacob D. Abernethy, Rafael M. Frongillo

ALT 2010 A Regularization Approach to Metrical Task Systems Jacob D. Abernethy, Peter L. Bartlett, Niv Buchbinder, Isabelle Stanton

COLT 2010 Can We Learn to Gamble Efficiently? Jacob D. Abernethy

MLJ 2010 Graph Regularization Methods for Web Spam Detection Jacob D. Abernethy, Olivier Chapelle, Carlos Castillo

NeurIPS 2010 Repeated Games Against Budgeted Adversaries Jacob D. Abernethy, Manfred K. Warmuth

COLT 2009 A Stochastic View of Optimal Regret Through Minimax Duality Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin

COLT 2009 An Efficient Bandit Algorithm for sqrt(T) Regret in Online Multiclass Prediction? Jacob D. Abernethy, Alexander Rakhlin

COLT 2009 Beating the Adaptive Bandit with High Probability Jacob D. Abernethy, Alexander Rakhlin

COLT 2009 Minimax Games with Bandits Jacob D. Abernethy, Manfred K. Warmuth

COLT 2008 Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization Jacob D. Abernethy, Elad Hazan, Alexander Rakhlin

COLT 2008 Optimal Stragies and Minimax Lower Bounds for Online Convex Games Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari

COLT 2008 When Random Play Is Optimal Against an Adversary Jacob D. Abernethy, Manfred K. Warmuth, Joel Yellin

COLT 2007 Multitask Learning with Expert Advice Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin

ICML 2007 Online Discovery of Similarity Mappings Alexander Rakhlin, Jacob D. Abernethy, Peter L. Bartlett

COLT 2006 Continuous Experts and the Binning Algorithm Jacob D. Abernethy, John Langford, Manfred K. Warmuth