Jacob D. Abernethy

120 papers A* 43A 4B 4C 1Journal 54Unranked 13

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Can Transformers Reason Logically? A Study in SAT Solving. ICML Leyan Pan, Vijay Ganesh, Jacob D. Abernethy, Chris Esposo, Wenke Lee
2025	J	`jnl`	SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms. CoRR Alex Havrilla, Edward Hughes, Mikayel Samvelyan, Jacob D. Abernethy
2024	B	`conf`	A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. ALT Jacob D. Abernethy, Alekh Agarwal, Teodor Vanislavov Marinov, Manfred K. Warmuth
2024	J	`jnl`	Can Transformers Reason Logically? A Study in SAT Solving. CoRR Leyan Pan, Vijay Ganesh, Jacob D. Abernethy, Chris Esposo, Wenke Lee
2024	A	`conf`	Extragradient Type Methods for Riemannian Variational Inequality Problems. AISTATS Zihao Hu, Guanghui Wang, Xi Wang, Andre Wibisono, Jacob D. Abernethy, Molei Tao
2024	A	`conf`	Lexicographic Optimization: Algorithms and Stability. AISTATS Jacob D. Abernethy, Robert E. Schapire, Umar Syed
2024	J	`jnl`	No-regret dynamics in the Fenchel game: a unified framework for algorithmic convex optimization. Math. Program. Jun-Kun Wang, Jacob D. Abernethy, Kfir Y. Levy
2023	J	`jnl`	A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. CoRR Jacob D. Abernethy, Alekh Agarwal, Teodor V. Marinov, Manfred K. Warmuth
2023	J	`jnl`	Accelerated Federated Optimization with Quantization. IEEE Data Eng. Bull. Yeojoon Youn, Bhuvesh Kumar, Jacob D. Abernethy
2023	—	`conf`	Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision. CogMI Feng Luo, Ling Liu, G. Geoff Wang, Vijay Kumar, Mark S. Ashton, Jacob D. Abernethy, Fatemeh Afghah, Matthew H. E. M. Browning, David Coyle, Philip M. Dames, Tom O'Halloran, James Hays, Patrick Hiesl, Chenfanfu Jiang, Puskar Khanal, Venkat Narayan Krovi, Sara Kuebbing, Nianyi Li, Jingjing Liang, Ninghao Liu, Steve McNulty, Christopher M. Oswalt, Neil Pederson, Demetri Terzopoulos, Christopher W. Woodall, Yongkai Wu, Jian Yang, Yin Yang, Liang Zhao
2023	J	`jnl`	Extragradient Type Methods for Riemannian Variational Inequality Problems. CoRR Zihao Hu, Guanghui Wang, Xi Wang, Andre Wibisono, Jacob D. Abernethy, Molei Tao
2023	A*	`conf`	Faster Margin Maximization Rates for Generic Optimization Methods. NeurIPS Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy
2023	J	`jnl`	Faster Margin Maximization Rates for Generic Optimization Methods. CoRR Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy
2023	A*	`conf`	Minimizing Dynamic Regret on Geodesic Metric Spaces. COLT Zihao Hu, Guanghui Wang, Jacob D. Abernethy
2023	J	`jnl`	Minimizing Dynamic Regret on Geodesic Metric Spaces. CoRR Zihao Hu, Guanghui Wang, Jacob D. Abernethy
2023	A*	`conf`	On Accelerated Perceptrons and Beyond. ICLR Guanghui Wang, Rafael Hanashiro, Etash Kumar Guha, Jacob D. Abernethy
2023	J	`jnl`	On Riemannian Projection-free Online Learning. CoRR Zihao Hu, Guanghui Wang, Jacob D. Abernethy
2023	J	`jnl`	On the Robustness of Epoch-Greedy in Multi-Agent Contextual Bandit Mechanisms. CoRR Yinglun Xu, Bhuvesh Kumar, Jacob D. Abernethy
2023	J	`jnl`	Randomized Quantization is All You Need for Differential Privacy in Federated Learning. CoRR Yeojoon Youn, Zihao Hu, Juba Ziani, Jacob D. Abernethy
2023	A*	`conf`	Riemannian Projection-free Online Learning. NeurIPS Zihao Hu, Guanghui Wang, Jacob D. Abernethy
2022	A*	`conf`	Active Sampling for Min-Max Fairness. ICML Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang
2022	A*	`conf`	ActiveHedge: Hedge meets Active Learning. ICML Bhuvesh Kumar, Jacob D. Abernethy, Venkatesh Saligrama
2022	A*	`conf`	Adaptive Oracle-Efficient Online Learning. NeurIPS Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy
2022	J	`jnl`	Adaptive Oracle-Efficient Online Learning. CoRR Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy
2022	J	`jnl`	On Accelerated Perceptrons and Beyond. CoRR Guanghui Wang, Rafael Hanashiro, Etash Kumar Guha, Jacob D. Abernethy
2021	A*	`conf`	A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network. ICML Jun-Kun Wang, Chi-Heng Lin, Jacob D. Abernethy
2021	J	`jnl`	A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness. CoRR Jacob D. Abernethy, Pranjal Awasthi, Satyen Kale
2021	J	`jnl`	Escaping Saddle Points Faster with Stochastic Momentum. CoRR Jun-Kun Wang, Chi-Heng Lin, Jacob D. Abernethy
2021	A*	`conf`	Fast Convergence of Fictitious Play for Diagonal Payoff Matrices. SODA Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono
2021	B	`conf`	Last-Iterate Convergence Rates for Min-Max Optimization: Convergence of Hamiltonian Gradient Descent and Consensus Optimization. ALT Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono
2021	J	`jnl`	No-Regret Dynamics in the Fenchel Game: A Unified Framework for Algorithmic Convex Optimization. CoRR Jun-Kun Wang, Jacob D. Abernethy, Kfir Y. Levy
2021	A*	`conf`	Observation-Free Attacks on Stochastic Bandits. NeurIPS Yinglun Xu, Bhuvesh Kumar, Jacob D. Abernethy
2021	C	`conf`	Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron. ACML Jun-Kun Wang, Jacob D. Abernethy
2020	J	`jnl`	Adaptive Sampling to Reduce Disparate Performance. CoRR Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Jie Zhang
2020	A*	`conf`	Conference on Learning Theory 2020: Preface. COLT Jacob D. Abernethy, Shivani Agarwal
2020	A*	`ed.`	Conference on Learning Theory, COLT 2020, 9-12 July 2020, Virtual Event [Graz, Austria]. COLT Jacob D. Abernethy, Shivani Agarwal
2020	A*	`conf`	Escaping Saddle Points Faster with Stochastic Momentum. ICLR Jun-Kun Wang, Chi-Heng Lin, Jacob D. Abernethy
2020	J	`jnl`	Linear Separation via Optimism. CoRR Rafael Hanashiro, Jacob D. Abernethy
2020	J	`jnl`	Online Kernel based Generative Adversarial Networks. CoRR Yeojoon Youn, Neil Thistlethwaite, Sang Keun Choe, Jacob D. Abernethy
2020	J	`jnl`	Provable Acceleration of Neural Net Training via Polyak's Momentum. CoRR Jun-Kun Wang, Jacob D. Abernethy
2020	J	`jnl`	Quickly Finding a Benign Region via Heavy Ball Momentum in Non-Convex Optimization. CoRR Jun-Kun Wang, Jacob D. Abernethy
2020	J	`jnl`	Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron. CoRR Jun-Kun Wang, Jacob D. Abernethy
2019	J	`jnl`	Competing Against Equilibria in Zero-Sum Games with Evolving Payoffs. CoRR Adrian Rivera Cardoso, Jacob D. Abernethy, He Wang, Huan Xu
2019	A*	`conf`	Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games. ICML Adrian Rivera Cardoso, Jacob D. Abernethy, He Wang, Huan Xu
2019	J	`jnl`	Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments. Mark. Sci. Kanishka Misra, Eric M. Schwartz, Jacob D. Abernethy
2019	J	`jnl`	Fictitious Play: Convergence, Smoothness, and Optimism. CoRR Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono
2019	J	`jnl`	Last-iterate convergence rates for min-max optimization. CoRR Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono
2019	A*	`conf`	Learning Auctions with Robust Incentive Guarantees. NeurIPS Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie Morgenstern
2019	A*	`conf`	Online Learning via the Differential Privacy Lens. NeurIPS Jacob D. Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari
2018	A*	`conf`	Acceleration through Optimistic No-Regret Dynamics. NeurIPS Jun-Kun Wang, Jacob D. Abernethy
2018	J	`jnl`	Acceleration through Optimistic No-Regret Dynamics. CoRR Jun-Kun Wang, Jacob D. Abernethy
2018	A*	`conf`	ActiveRemediation: The Search for Lead Pipes in Flint, Michigan. KDD Jacob D. Abernethy, Alex Chojnacki, Arya Farahi, Eric M. Schwartz, Jared Webb
2018	J	`jnl`	ActiveRemediation: The Search for Lead Pipes in Flint, Michigan. CoRR Jacob D. Abernethy, Alex Chojnacki, Arya Farahi, Eric M. Schwartz, Jared Webb
2018	A*	`conf`	Faster Rates for Convex-Concave Games. COLT Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang
2018	J	`jnl`	Faster Rates for Convex-Concave Games. CoRR Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang
2017	A*	`conf`	A Data Science Approach to Understanding Residential Water Contamination in Flint. KDD Alex Chojnacki, Chengyu Dai, Arya Farahi, Guangsha Shi, Jared Webb, Daniel T. Zhang, Jacob D. Abernethy, Eric M. Schwartz
2017	J	`jnl`	A Data Science Approach to Understanding Residential Water Contamination in Flint. CoRR Alex Chojnacki, Chengyu Dai, Arya Farahi, Guangsha Shi, Jared Webb, Daniel T. Zhang, Jacob D. Abernethy, Eric M. Schwartz
2017	J	`jnl`	Addendum to "A Market Framework for Eliciting Private Data". CoRR Bo Waggoner, Rafael M. Frongillo, Jacob D. Abernethy
2017	J	`jnl`	How to Train Your DRAGAN. CoRR Naveen Kodali, Jacob D. Abernethy, James Hays, Zsolt Kira
2017	—	`conf`	On Frank-Wolfe and Equilibrium Computation. NIPS Jacob D. Abernethy, Jun-Kun Wang
2017	J	`jnl`	Online Learning via Differential Privacy. CoRR Jacob D. Abernethy, Chansoo Lee, Audra McMillan, Ambuj Tewari
2016	—	`conf`	Analysing RateMyProfessors Evaluations Across Institutions, Disciplines, and Cultures: The Tell-Tale Signs of a Good Professor. SocInfo (1) Mahmoud Azab, Rada Mihalcea, Jacob D. Abernethy
2016	J	`jnl`	Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences. CoRR 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
2016	A*	`conf`	Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier. ICML Jacob D. Abernethy, Elad Hazan
2016	J	`jnl`	Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing. CoRR 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, Sheng Yang
2016	A*	`conf`	Rate of Price Discovery in Iterative Combinatorial Auctions. EC Jacob D. Abernethy, Sébastien Lahaie, Matus Telgarsky
2016	—	`conf`	Threshold Bandits, With and Without Censored Feedback. NIPS Jacob D. Abernethy, Kareem Amin, Ruihao Zhu
2016	A	`conf`	Utilizing high-dimensional features for real-time robotic applications: Reducing the curse of dimensionality for recursive Bayesian estimation. IROS Jie Li, Paul Ozog, Jacob D. Abernethy, Ryan M. Eustice, Matthew Johnson-Roberson
2015	—	`conf`	A Market Framework for Eliciting Private Data. NIPS Bo Waggoner, Rafael M. Frongillo, Jacob D. Abernethy
2015	J	`jnl`	Actively Purchasing Data for Learning. CoRR Jacob D. Abernethy, Yiling Chen, Chien-Ju Ho, Bo Waggoner
2015	J	`jnl`	Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier. CoRR Jacob D. Abernethy, Elad Hazan
2015	—	`conf`	Fighting Bandits with a New Kind of Smoothness. NIPS Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari
2015	J	`jnl`	Fighting Bandits with a New Kind of Smoothness. CoRR Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari
2015	A	`conf`	Financialized methods for market-based multi-sensor fusion. IROS Jacob D. Abernethy, Matthew Johnson-Roberson
2015	A*	`conf`	Low-Cost Learning via Active Data Procurement. EC Jacob D. Abernethy, Yiling Chen, Chien-Ju Ho, Bo Waggoner
2015	J	`jnl`	Rate of Price Discovery in Iterative Combinatorial Auctions. CoRR Jacob D. Abernethy, Sébastien Lahaie, Matus Telgarsky
2014	A*	`conf`	A general volume-parameterized market making framework. EC Jacob D. Abernethy, Rafael M. Frongillo, Xiaolong Li, Jennifer Wortman Vaughan
2014	J	`jnl`	Information Aggregation in Exponential Family Markets. CoRR Jacob D. Abernethy, Sindhu Kutty, Sébastien Lahaie, Rahul Sami
2014	A*	`conf`	Information aggregation in exponential family markets. EC Jacob D. Abernethy, Sindhu Kutty, Sébastien Lahaie, Rahul Sami
2014	B	`conf`	Jamming defense against a resource-replenishing adversary in multi-channel wireless systems. WiOpt Qingsi Wang, Shang-Pin Sheng, Jacob D. Abernethy, Mingyan Liu
2014	J	`jnl`	On risk measures, market making, and exponential families. SIGecom Exch. Jacob D. Abernethy, Rafael M. Frongillo, Sindhu Kutty
2014	A*	`conf`	Online Linear Optimization via Smoothing. COLT Jacob D. Abernethy, Chansoo Lee, Abhinav Sinha, Ambuj Tewari
2014	J	`jnl`	Online Linear Optimization via Smoothing. CoRR Jacob D. Abernethy, Chansoo Lee, Abhinav Sinha, Ambuj Tewari
2013	—	`conf`	Adaptive Market Making via Online Learning. NIPS Jacob D. Abernethy, Satyen Kale
2013	J	`jnl`	Efficient Market Making via Convex Optimization, and a Connection to Online Learning. ACM Trans. Economics and Comput. Jacob D. Abernethy, Yiling Chen, Jennifer Wortman Vaughan
2013	—	`conf`	How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal. NIPS Jacob D. Abernethy, Peter L. Bartlett, Rafael M. Frongillo, Andre Wibisono
2013	—	`conf`	Large-Scale Bandit Problems and KWIK Learning. ICML (1) Jacob D. Abernethy, Kareem Amin, Michael J. Kearns, Moez Draief
2013	—	`conf`	Minimax Optimal Algorithms for Unconstrained Linear Optimization. NIPS H. Brendan McMahan, Jacob D. Abernethy
2012	A*	`conf`	A Characterization of Scoring Rules for Linear Properties. COLT Jacob D. Abernethy, Rafael M. Frongillo
2012	J	`jnl`	Interior-Point Methods for Full-Information and Bandit Online Learning. IEEE Trans. Inf. Theory Jacob D. Abernethy, Elad Hazan, Alexander Rakhlin
2012	J	`jnl`	Minimax Option Pricing Meets Black-Scholes in the Limit CoRR Jacob D. Abernethy, Rafael M. Frongillo, Andre Wibisono
2012	A*	`conf`	Minimax option pricing meets black-scholes in the limit. STOC Jacob D. Abernethy, Rafael M. Frongillo, Andre Wibisono
2011	J	`jnl`	A Collaborative Mechanism for Crowdsourcing Prediction Problems CoRR Jacob D. Abernethy, Rafael M. Frongillo
2011	—	`conf`	A Collaborative Mechanism for Crowdsourcing Prediction Problems. NIPS Jacob D. Abernethy, Rafael M. Frongillo
2011	A*	`conf`	An optimization-based framework for automated market-making. EC Jacob D. Abernethy, Yiling Chen, Jennifer Wortman Vaughan
2011	A*	`conf`	Blackwell Approachability and No-Regret Learning are Equivalent. COLT Jacob D. Abernethy, Peter L. Bartlett, Elad Hazan
2011	A*	`conf`	Does an Efficient Calibrated Forecasting Strategy Exist? COLT Jacob D. Abernethy, Shie Mannor
2011	—	`—`	Sequential Decision Making in Non-stochastic Environments. Jacob D. Abernethy
2010	B	`conf`	A Regularization Approach to Metrical Task Systems. ALT Jacob D. Abernethy, Peter L. Bartlett, Niv Buchbinder, Isabelle Stanton
2010	J	`jnl`	An Optimization-Based Framework for Automated Market-Making CoRR Jacob D. Abernethy, Yiling Chen, Jennifer Wortman Vaughan
2010	J	`jnl`	Blackwell Approachability and Low-Regret Learning are Equivalent CoRR Jacob D. Abernethy, Peter L. Bartlett, Elad Hazan
2010	A*	`conf`	Can We Learn to Gamble Efficiently? COLT Jacob D. Abernethy
2010	J	`jnl`	Graph regularization methods for Web spam detection. Mach. Learn. Jacob D. Abernethy, Olivier Chapelle, Carlos Castillo
2010	—	`conf`	Repeated Games against Budgeted Adversaries. NIPS Jacob D. Abernethy, Manfred K. Warmuth
2009	J	`jnl`	A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization. J. Mach. Learn. Res. Jacob D. Abernethy, Francis R. Bach, Theodoros Evgeniou, Jean-Philippe Vert
2009	J	`jnl`	A Stochastic View of Optimal Regret through Minimax Duality CoRR Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin
2009	A*	`conf`	A Stochastic View of Optimal Regret through Minimax Duality. COLT Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin
2009	A*	`conf`	An Efficient Bandit Algorithm for sqrt(T) Regret in Online Multiclass Prediction?. COLT Jacob D. Abernethy, Alexander Rakhlin
2009	A*	`conf`	Beating the Adaptive Bandit with High Probability. COLT Jacob D. Abernethy, Alexander Rakhlin
2009	A*	`conf`	Minimax Games with Bandits. COLT Jacob D. Abernethy, Manfred K. Warmuth
2008	J	`jnl`	A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization CoRR Francis R. Bach, Jacob D. Abernethy, Jean-Philippe Vert, Theodoros Evgeniou
2008	A*	`conf`	Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization. COLT Jacob D. Abernethy, Elad Hazan, Alexander Rakhlin
2008	J	`jnl`	Eliciting Consumer Preferences Using Robust Adaptive Choice Questionnaires. IEEE Trans. Knowl. Data Eng. Jacob D. Abernethy, Theodoros Evgeniou, Olivier Toubia, Jean-Philippe Vert
2008	A*	`conf`	Optimal Stragies and Minimax Lower Bounds for Online Convex Games. COLT Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari
2008	—	`conf`	Web spam identification through content and hyperlinks. AIRWeb Jacob D. Abernethy, Olivier Chapelle, Carlos Castillo
2008	A*	`conf`	When Random Play is Optimal Against an Adversary. COLT Jacob D. Abernethy, Manfred K. Warmuth, Joel Yellin
2007	A*	`conf`	Multitask Learning with Expert Advice. COLT Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin
2007	A*	`conf`	Online discovery of similarity mappings. ICML Alexander Rakhlin, Jacob D. Abernethy, Peter L. Bartlett
2006	A*	`conf`	Continuous Experts and the Binning Algorithm. COLT Jacob D. Abernethy, John Langford, Manfred K. Warmuth
2006	J	`jnl`	Low-rank matrix factorization with attributes CoRR Jacob D. Abernethy, Francis R. Bach, Theodoros Evgeniou, Jean-Philippe Vert