Continuous Experts and the Binning Algorithm. - Jacob D. Abernethy; John Langford 001; Manfred K. Warmuth (COLT, 2006)

Low-rank matrix factorization with attributes - Jacob D. Abernethy; Francis R. Bach; Theodoros Evgeniou; Jean-Philippe Vert (CoRR, 2006)

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

Online discovery of similarity mappings. - Alexander Rakhlin; Jacob D. Abernethy; Peter L. Bartlett (ICML, 2007)

Web spam identification through content and hyperlinks. - Jacob D. Abernethy; Olivier Chapelle; Carlos Castillo 001 (AIRWeb, 2008)

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

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

When Random Play is Optimal Against an Adversary. - Jacob D. Abernethy; Manfred K. Warmuth; Joel Yellin (COLT, 2008)

Eliciting Consumer Preferences Using Robust Adaptive Choice Questionnaires. - Jacob D. Abernethy; Theodoros Evgeniou; Olivier Toubia; Jean-Philippe Vert (IEEE Trans. Knowl. Data Eng., 2008)

A Stochastic View of Optimal Regret through Minimax Duality. - Jacob D. Abernethy; Alekh Agarwal; Peter L. Bartlett; Alexander Rakhlin (COLT, 2009)

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

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

Minimax Games with Bandits. - Jacob D. Abernethy; Manfred K. Warmuth (COLT, 2009)

A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization. - Jacob D. Abernethy; Francis R. Bach; Theodoros Evgeniou; Jean-Philippe Vert (J. Mach. Learn. Res., 2009)

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

Can We Learn to Gamble Efficiently? - Jacob D. Abernethy (COLT, 2010)

Repeated Games against Budgeted Adversaries. - Jacob D. Abernethy; Manfred K. Warmuth (NIPS, 2010)

Blackwell Approachability and Low-Regret Learning are Equivalent - Jacob D. Abernethy; Peter L. Bartlett; Elad Hazan (CoRR, 2010)

Graph regularization methods for Web spam detection. - Jacob D. Abernethy; Olivier Chapelle; Carlos Castillo 001 (Mach. Learn., 2010)

A Collaborative Mechanism for Crowdsourcing Prediction Problems. - Jacob D. Abernethy; Rafael M. Frongillo (NIPS, 2011)

An optimization-based framework for automated market-making. - Jacob D. Abernethy; Yiling Chen 001; Jennifer Wortman Vaughan (EC, 2011)

Blackwell Approachability and No-Regret Learning are Equivalent. - Jacob D. Abernethy; Peter L. Bartlett; Elad Hazan (COLT, 2011)

Does an Efficient Calibrated Forecasting Strategy Exist? - Jacob D. Abernethy; Shie Mannor (COLT, 2011)

Sequential Decision Making in Non-stochastic Environments. - Jacob D. Abernethy (2011)

Minimax option pricing meets black-scholes in the limit. - Jacob D. Abernethy; Rafael M. Frongillo; Andre Wibisono (STOC, 2012)

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

Interior-Point Methods for Full-Information and Bandit Online Learning. - Jacob D. Abernethy; Elad Hazan; Alexander Rakhlin (IEEE Trans. Inf. Theory, 2012)

Large-Scale Bandit Problems and KWIK Learning. - Jacob D. Abernethy; Kareem Amin 002; Michael J. Kearns; Moez Draief (ICML (1), 2013)

How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal. - Jacob D. Abernethy; Peter L. Bartlett; Rafael M. Frongillo; Andre Wibisono (NIPS, 2013)

Adaptive Market Making via Online Learning. - Jacob D. Abernethy; Satyen Kale (NIPS, 2013)

Minimax Optimal Algorithms for Unconstrained Linear Optimization. - H. Brendan McMahan; Jacob D. Abernethy (NIPS, 2013)

Efficient Market Making via Convex Optimization, and a Connection to Online Learning. - Jacob D. Abernethy; Yiling Chen 001; Jennifer Wortman Vaughan (ACM Trans. Economics and Comput., 2013)

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

A general volume-parameterized market making framework. - Jacob D. Abernethy; Rafael M. Frongillo; Xiaolong Li; Jennifer Wortman Vaughan (EC, 2014)

Information aggregation in exponential family markets. - Jacob D. Abernethy; Sindhu Kutty; Sébastien Lahaie; Rahul Sami (EC, 2014)

Jamming defense against a resource-replenishing adversary in multi-channel wireless systems. - Qingsi Wang; Shang-Pin Sheng; Jacob D. Abernethy; Mingyan Liu (WiOpt, 2014)

On risk measures, market making, and exponential families. - Jacob D. Abernethy; Rafael M. Frongillo; Sindhu Kutty (SIGecom Exch., 2014)

Financialized methods for market-based multi-sensor fusion. - Jacob D. Abernethy; Matthew Johnson-Roberson (IROS, 2015)

Fighting Bandits with a New Kind of Smoothness. - Jacob D. Abernethy; Chansoo Lee; Ambuj Tewari (NIPS, 2015)

A Market Framework for Eliciting Private Data. - Bo Waggoner; Rafael M. Frongillo; Jacob D. Abernethy (NIPS, 2015)

Low-Cost Learning via Active Data Procurement. - Jacob D. Abernethy; Yiling Chen 001; Chien-Ju Ho; Bo Waggoner (EC, 2015)

Actively Purchasing Data for Learning. - Jacob D. Abernethy; Yiling Chen 001; Chien-Ju Ho; Bo Waggoner (CoRR, 2015)

Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier. - Jacob D. Abernethy; Elad Hazan (ICML, 2016)

Utilizing high-dimensional features for real-time robotic applications: Reducing the curse of dimensionality for recursive Bayesian estimation. - Jie Li 017; Paul Ozog; Jacob D. Abernethy; Ryan M. Eustice; Matthew Johnson-Roberson (IROS, 2016)

Threshold Bandits, With and Without Censored Feedback. - Jacob D. Abernethy; Kareem Amin 002; Ruihao Zhu (NIPS, 2016)

Rate of Price Discovery in Iterative Combinatorial Auctions. - Jacob D. Abernethy; Sébastien Lahaie; Matus Telgarsky (EC, 2016)

Analysing RateMyProfessors Evaluations Across Institutions, Disciplines, and Cultures: The Tell-Tale Signs of a Good Professor. - Mahmoud Azab; Rada Mihalcea; Jacob D. Abernethy (SocInfo (1), 2016)

Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences. - Jacob D. Abernethy; Cyrus Anderson; Alex Chojnacki; Chengyu Dai; John Dryden; Eric M. Schwartz; Wenbo Shen; Jonathan C. Stroud; Laura Wendlandt; Sheng Yang; Daniel T. Zhang (CoRR, 2016)

Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing. - Jacob D. Abernethy; Cyrus Anderson; Chengyu Dai; Arya Farahi; Linh Nguyen; Adam Rauh; Eric M. Schwartz; Wenbo Shen; Guangsha Shi; Jonathan C. Stroud; Xinyu Tan; Jared Webb; et al. (CoRR, 2016)

A Data Science Approach to Understanding Residential Water Contamination in Flint. - Alex Chojnacki; Chengyu Dai; Arya Farahi; Guangsha Shi; Jared Webb; Daniel T. Zhang; Jacob D. Abernethy; Eric M. Schwartz (KDD, 2017)

On Frank-Wolfe and Equilibrium Computation. - Jacob D. Abernethy; Jun-Kun Wang (NIPS, 2017)

How to Train Your DRAGAN. - Naveen Kodali; Jacob D. Abernethy; James Hays; Zsolt Kira (CoRR, 2017)

Addendum to "A Market Framework for Eliciting Private Data". - Bo Waggoner; Rafael M. Frongillo; Jacob D. Abernethy (CoRR, 2017)

Online Learning via Differential Privacy. - Jacob D. Abernethy; Chansoo Lee; Audra McMillan; Ambuj Tewari (CoRR, 2017)

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

ActiveRemediation: The Search for Lead Pipes in Flint, Michigan. - Jacob D. Abernethy; Alex Chojnacki; Arya Farahi; Eric M. Schwartz; Jared Webb (KDD, 2018)

Acceleration through Optimistic No-Regret Dynamics. - Jun-Kun Wang; Jacob D. Abernethy (NeurIPS, 2018)

Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games. - Adrian Rivera Cardoso; Jacob D. Abernethy; He Wang; Huan Xu (ICML, 2019)

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

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

Last-iterate convergence rates for min-max optimization. - Jacob D. Abernethy; Kevin A. Lai; Andre Wibisono (CoRR, 2019)

Competing Against Equilibria in Zero-Sum Games with Evolving Payoffs. - Adrian Rivera Cardoso; Jacob D. Abernethy; He Wang; Huan Xu (CoRR, 2019)

Fictitious Play: Convergence, Smoothness, and Optimism. - Jacob D. Abernethy; Kevin A. Lai; Andre Wibisono (CoRR, 2019)

Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments. - Kanishka Misra; Eric M. Schwartz; Jacob D. Abernethy (Mark. Sci., 2019)

Conference on Learning Theory 2020: Preface. - Jacob D. Abernethy; Shivani Agarwal 001 (COLT, 2020)

Escaping Saddle Points Faster with Stochastic Momentum. - Jun-Kun Wang; Chi-Heng Lin; Jacob D. Abernethy (ICLR, 2020)

Adaptive Sampling to Reduce Disparate Performance. - Jacob D. Abernethy; Pranjal Awasthi; Matthäus Kleindessner; Jamie Morgenstern; Jie Zhang 096 (CoRR, 2020)

Online Kernel based Generative Adversarial Networks. - Yeojoon Youn; Neil Thistlethwaite; Sang Keun Choe; Jacob D. Abernethy (CoRR, 2020)

Quickly Finding a Benign Region via Heavy Ball Momentum in Non-Convex Optimization. - Jun-Kun Wang; Jacob D. Abernethy (CoRR, 2020)

Provable Acceleration of Neural Net Training via Polyak's Momentum. - Jun-Kun Wang; Jacob D. Abernethy (CoRR, 2020)

Linear Separation via Optimism. - Rafael Hanashiro; Jacob D. Abernethy (CoRR, 2020)

Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron. - Jun-Kun Wang; Jacob D. Abernethy (ACML, 2021)

Last-Iterate Convergence Rates for Min-Max Optimization: Convergence of Hamiltonian Gradient Descent and Consensus Optimization. - Jacob D. Abernethy; Kevin A. Lai; Andre Wibisono (ALT, 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 (ICML, 2021)

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

Fast Convergence of Fictitious Play for Diagonal Payoff Matrices. - Jacob D. Abernethy; Kevin A. Lai; Andre Wibisono (SODA, 2021)

A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness. - Jacob D. Abernethy; Pranjal Awasthi; Satyen Kale (CoRR, 2021)

Active Sampling for Min-Max Fairness. - Jacob D. Abernethy; Pranjal Awasthi; Matthäus Kleindessner; Jamie Morgenstern; Chris Russell 001; Jie Zhang 096 (ICML, 2022)

ActiveHedge: Hedge meets Active Learning. - Bhuvesh Kumar; Jacob D. Abernethy; Venkatesh Saligrama (ICML, 2022)

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

Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision. - Feng Luo 001; Ling Liu; G. Geoff Wang; Vijay Kumar 001; Mark S. Ashton; Jacob D. Abernethy; Fatemeh Afghah; Matthew H. E. M. Browning; David Coyle; Philip M. Dames; Tom O'Halloran; James Hays; et al. (CogMI, 2023)

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

On Accelerated Perceptrons and Beyond. - Guanghui Wang; Rafael Hanashiro; Etash Kumar Guha; Jacob D. Abernethy (ICLR, 2023)

Riemannian Projection-free Online Learning. - Zihao Hu; Guanghui Wang; Jacob D. Abernethy (NeurIPS, 2023)

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

Randomized Quantization is All You Need for Differential Privacy in Federated Learning. - Yeojoon Youn; Zihao Hu; Juba Ziani; Jacob D. Abernethy (CoRR, 2023)

On the Robustness of Epoch-Greedy in Multi-Agent Contextual Bandit Mechanisms. - Yinglun Xu; Bhuvesh Kumar; Jacob D. Abernethy (CoRR, 2023)

Accelerated Federated Optimization with Quantization. - Yeojoon Youn; Bhuvesh Kumar; Jacob D. Abernethy (IEEE Data Eng. Bull., 2023)

Lexicographic Optimization: Algorithms and Stability. - Jacob D. Abernethy; Robert E. Schapire; Umar Syed (AISTATS, 2024)

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

A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. - Jacob D. Abernethy; Alekh Agarwal; Teodor Vanislavov Marinov; Manfred K. Warmuth (ALT, 2024)

Can Transformers Reason Logically? A Study in SAT Solving. - Leyan Pan; Vijay Ganesh 001; Jacob D. Abernethy; Chris Esposo; Wenke Lee (CoRR, 2024)

No-regret dynamics in the Fenchel game: a unified framework for algorithmic convex optimization. - Jun-Kun Wang; Jacob D. Abernethy; Kfir Y. Levy (Math. Program., 2024)

SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms. - Alex Havrilla; Edward Hughes 001; Mikayel Samvelyan; Jacob D. Abernethy (CoRR, 2025)