| 2025 | ICML | Can Transformers Reason Logically? A Study in SAT Solving. | Leyan Pan, Vijay Ganesh, Jacob D. Abernethy, Chris Esposo, Wenke Lee |
| 2024 | AISTATS | Lexicographic Optimization: Algorithms and Stability. | Jacob D. Abernethy, Robert E. Schapire, Umar Syed |
| 2024 | AISTATS | Extragradient Type Methods for Riemannian Variational Inequality Problems. | Zihao Hu, Guanghui Wang, Xi Wang, Andre Wibisono, Jacob D. Abernethy, Molei Tao |
| 2024 | ALT | A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. | Jacob D. Abernethy, Alekh Agarwal, Teodor Vanislavov Marinov, Manfred K. Warmuth |
| 2023 | COLT | Minimizing Dynamic Regret on Geodesic Metric Spaces. | Zihao Hu, Guanghui Wang, Jacob D. Abernethy |
| 2023 | ICLR | On Accelerated Perceptrons and Beyond. | Guanghui Wang, Rafael Hanashiro, Etash Kumar Guha, Jacob D. Abernethy |
| 2022 | ICML | Active Sampling for Min-Max Fairness. | Jacob D. Abernethy, Pranjal Awasthi, Matthus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang |
| 2022 | ICML | ActiveHedge: Hedge meets Active Learning. | Bhuvesh Kumar, Jacob D. Abernethy, Venkatesh Saligrama |
| 2021 | ACML | Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron. | Jun-Kun Wang, Jacob D. Abernethy |
| 2021 | ALT | Last-Iterate Convergence Rates for Min-Max Optimization: Convergence of Hamiltonian Gradient Descent and Consensus Optimization. | Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono |
| 2021 | ICML | 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 |
| 2021 | SODA | Fast Convergence of Fictitious Play for Diagonal Payoff Matrices. | Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono |
| 2020 | COLT | Conference on Learning Theory 2020: Preface. | Jacob D. Abernethy, Shivani Agarwal |
| 2020 | ICLR | Escaping Saddle Points Faster with Stochastic Momentum. | Jun-Kun Wang, Chi-Heng Lin, Jacob D. Abernethy |
| 2019 | ICML | Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games. | Adrian Rivera Cardoso, Jacob D. Abernethy, He Wang, Huan Xu |
| 2018 | COLT | Faster Rates for Convex-Concave Games. | Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang |
| 2018 | KDD | ActiveRemediation: The Search for Lead Pipes in Flint, Michigan. | Jacob D. Abernethy, Alex Chojnacki, Arya Farahi, Eric M. Schwartz, Jared Webb |
| 2017 | KDD | 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 |
| 2016 | ICML | Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier. | Jacob D. Abernethy, Elad Hazan |
| 2016 | IROS | Utilizing high-dimensional features for real-time robotic applications: Reducing the curse of dimensionality for recursive Bayesian estimation. | Jie Li, Paul Ozog, Jacob D. Abernethy, Ryan M. Eustice, Matthew Johnson-Roberson |
| 2015 | IROS | Financialized methods for market-based multi-sensor fusion. | Jacob D. Abernethy, Matthew Johnson-Roberson |
| 2014 | COLT | Online Linear Optimization via Smoothing. | Jacob D. Abernethy, Chansoo Lee, Abhinav Sinha, Ambuj Tewari |
| 2014 | WiOpt | Jamming defense against a resource-replenishing adversary in multi-channel wireless systems. | Qingsi Wang, Shang-Pin Sheng, Jacob D. Abernethy, Mingyan Liu |
| 2013 | ICML | Large-Scale Bandit Problems and KWIK Learning. | Jacob D. Abernethy, Kareem Amin, Michael J. Kearns, Moez Draief |
| 2012 | STOC | Minimax option pricing meets black-scholes in the limit. | Jacob D. Abernethy, Rafael M. Frongillo, Andre Wibisono |
| 2010 | ALT | A Regularization Approach to Metrical Task Systems. | Jacob D. Abernethy, Peter L. Bartlett, Niv Buchbinder, Isabelle Stanton |
| 2010 | COLT | Can We Learn to Gamble Efficiently? | Jacob D. Abernethy |
| 2009 | COLT | A Stochastic View of Optimal Regret through Minimax Duality. | Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin |
| 2009 | COLT | Beating the Adaptive Bandit with High Probability. | Jacob D. Abernethy, Alexander Rakhlin |
| 2009 | COLT | An Efficient Bandit Algorithm for sqrt(T) Regret in Online Multiclass Prediction?. | Jacob D. Abernethy, Alexander Rakhlin |
| 2009 | COLT | Minimax Games with Bandits. | Jacob D. Abernethy, Manfred K. Warmuth |
| 2008 | COLT | Optimal Stragies and Minimax Lower Bounds for Online Convex Games. | Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari |
| 2008 | COLT | Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization. | Jacob D. Abernethy, Elad Hazan, Alexander Rakhlin |
| 2008 | COLT | When Random Play is Optimal Against an Adversary. | Jacob D. Abernethy, Manfred K. Warmuth, Joel Yellin |
| 2007 | COLT | Multitask Learning with Expert Advice. | Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin |
| 2007 | ICML | Online discovery of similarity mappings. | Alexander Rakhlin, Jacob D. Abernethy, Peter L. Bartlett |
| 2006 | COLT | Continuous Experts and the Binning Algorithm. | Jacob D. Abernethy, John Langford, Manfred K. Warmuth |