Jacob D. Abernethy

ORCID

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 72

Top Venues

Publications

Jun-Kun Wang, Jacob D. Abernethy, Kfir Y. Levy
No-regret dynamics in the Fenchel game: a unified framework for algorithmic convex optimization. Math. Program. 205 (1) (2024)
Jacob D. Abernethy, Alekh Agarwal, Teodor Vanislavov Marinov, Manfred K. Warmuth
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. ALT (2024)
Zihao Hu, Guanghui Wang, Xi Wang, Andre Wibisono, Jacob D. Abernethy, Molei Tao
Extragradient Type Methods for Riemannian Variational Inequality Problems. AISTATS (2024)
Jacob D. Abernethy, Robert E. Schapire, Umar Syed
Lexicographic Optimization: Algorithms and Stability. AISTATS (2024)
Zihao Hu, Guanghui Wang, Jacob D. Abernethy
Minimizing Dynamic Regret on Geodesic Metric Spaces. CoRR (2023)
Zihao Hu, Guanghui Wang, Jacob D. Abernethy
On Riemannian Projection-free Online Learning. CoRR (2023)
Yeojoon Youn, Zihao Hu, Juba Ziani, Jacob D. Abernethy
Randomized Quantization is All You Need for Differential Privacy in Federated Learning. CoRR (2023)
Jacob D. Abernethy, Alekh Agarwal, Teodor V. Marinov, Manfred K. Warmuth
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. CoRR (2023)
Zihao Hu, Guanghui Wang, Jacob D. Abernethy
Minimizing Dynamic Regret on Geodesic Metric Spaces. COLT (2023)
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 Heisl, 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
Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision. CogMI (2023)
Yeojoon Youn, Bhuvesh Kumar, Jacob D. Abernethy
Accelerated Federated Optimization with Quantization. IEEE Data Eng. Bull. 46 (1) (2023)
Yinglun Xu, Bhuvesh Kumar, Jacob D. Abernethy
On the Robustness of Epoch-Greedy in Multi-Agent Contextual Bandit Mechanisms. CoRR (2023)
Zihao Hu, Guanghui Wang, Xi Wang, Andre Wibisono, Jacob D. Abernethy, Molei Tao
Extragradient Type Methods for Riemannian Variational Inequality Problems. CoRR (2023)
Guanghui Wang, Rafael Hanashiro, Etash Kumar Guha, Jacob D. Abernethy
On Accelerated Perceptrons and Beyond. ICLR (2023)
Zihao Hu, Guanghui Wang, Jacob D. Abernethy
Riemannian Projection-free Online Learning. NeurIPS (2023)
Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy
Faster Margin Maximization Rates for Generic Optimization Methods. CoRR (2023)
Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy
Faster Margin Maximization Rates for Generic Optimization Methods. NeurIPS (2023)
Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy
Adaptive Oracle-Efficient Online Learning. NeurIPS (2022)
Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy
Adaptive Oracle-Efficient Online Learning. CoRR (2022)
Guanghui Wang, Rafael Hanashiro, Etash Guha, Jacob D. Abernethy
On Accelerated Perceptrons and Beyond. CoRR (2022)
Bhuvesh Kumar, Jacob D. Abernethy, Venkatesh Saligrama
ActiveHedge: Hedge meets Active Learning. ICML (2022)
Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang
Active Sampling for Min-Max Fairness. ICML (2022)
Jun-Kun Wang, Chi-Heng Lin, Jacob D. Abernethy
A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network. ICML (2021)
Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono
Last-Iterate Convergence Rates for Min-Max Optimization: Convergence of Hamiltonian Gradient Descent and Consensus Optimization. ALT (2021)
Jun-Kun Wang, Jacob D. Abernethy
Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron. ACML (2021)
Jun-Kun Wang, Chi-Heng Lin, Jacob D. Abernethy
Escaping Saddle Points Faster with Stochastic Momentum. CoRR (2021)
Jacob D. Abernethy, Pranjal Awasthi, Satyen Kale
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness. CoRR (2021)
Jun-Kun Wang, Jacob D. Abernethy, Kfir Y. Levy
No-Regret Dynamics in the Fenchel Game: A Unified Framework for Algorithmic Convex Optimization. CoRR (2021)
Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono
Fast Convergence of Fictitious Play for Diagonal Payoff Matrices. SODA (2021)
Yinglun Xu, Bhuvesh Kumar, Jacob D. Abernethy
Observation-Free Attacks on Stochastic Bandits. NeurIPS (2021)
Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Jie Zhang
Adaptive Sampling to Reduce Disparate Performance. CoRR (2020)
Jacob D. Abernethy, Shivani Agarwal
Conference on Learning Theory 2020: Preface. COLT (2020)
Jun-Kun Wang, Jacob D. Abernethy
Quickly Finding a Benign Region via Heavy Ball Momentum in Non-Convex Optimization. CoRR (2020)
Jun-Kun Wang, Jacob D. Abernethy
Provable Acceleration of Neural Net Training via Polyak's Momentum. CoRR (2020)
Jun-Kun Wang, Chi-Heng Lin, Jacob D. Abernethy
Escaping Saddle Points Faster with Stochastic Momentum. ICLR (2020)
Rafael Hanashiro, Jacob D. Abernethy
Linear Separation via Optimism. CoRR (2020)
Yeojoon Youn, Neil Thistlethwaite, Sang Keun Choe, Jacob D. Abernethy
Online Kernel based Generative Adversarial Networks. CoRR (2020)
Jun-Kun Wang, Jacob D. Abernethy
Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron. CoRR (2020)
Adrian Rivera Cardoso, Jacob D. Abernethy, He Wang, Huan Xu
Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games. ICML (2019)
Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie Morgenstern
Learning Auctions with Robust Incentive Guarantees. NeurIPS (2019)
Kanishka Misra, Eric M. Schwartz, Jacob D. Abernethy
Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments. Mark. Sci. 38 (2) (2019)
Jacob D. Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari
Online Learning via the Differential Privacy Lens. NeurIPS (2019)
Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono
Fictitious Play: Convergence, Smoothness, and Optimism. CoRR (2019)
Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono
Last-iterate convergence rates for min-max optimization. CoRR (2019)
Adrian Rivera Cardoso, Jacob D. Abernethy, He Wang, Huan Xu
Competing Against Equilibria in Zero-Sum Games with Evolving Payoffs. CoRR (2019)
Jun-Kun Wang, Jacob D. Abernethy
Acceleration through Optimistic No-Regret Dynamics. NeurIPS (2018)
Jun-Kun Wang, Jacob D. Abernethy
Acceleration through Optimistic No-Regret Dynamics. CoRR (2018)
Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang
Faster Rates for Convex-Concave Games. CoRR (2018)
Jacob D. Abernethy, Alex Chojnacki, Arya Farahi, Eric M. Schwartz, Jared Webb
ActiveRemediation: The Search for Lead Pipes in Flint, Michigan. CoRR (2018)
Jacob D. Abernethy, Alex Chojnacki, Arya Farahi, Eric M. Schwartz, Jared Webb
ActiveRemediation: The Search for Lead Pipes in Flint, Michigan. KDD (2018)
Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang
Faster Rates for Convex-Concave Games. COLT (2018)
Alex Chojnacki, Chengyu Dai, Arya Farahi, Guangsha Shi, Jared Webb, Daniel T. Zhang, Jacob D. Abernethy, Eric M. Schwartz
A Data Science Approach to Understanding Residential Water Contamination in Flint. KDD (2017)
Jacob D. Abernethy, Jun-Kun Wang
On Frank-Wolfe and Equilibrium Computation. NIPS (2017)
Alex Chojnacki, Chengyu Dai, Arya Farahi, Guangsha Shi, Jared Webb, Daniel T. Zhang, Jacob D. Abernethy, Eric M. Schwartz
A Data Science Approach to Understanding Residential Water Contamination in Flint. CoRR (2017)
Bo Waggoner, Rafael M. Frongillo, Jacob D. Abernethy
Addendum to "A Market Framework for Eliciting Private Data". CoRR (2017)
Naveen Kodali, Jacob D. Abernethy, James Hays, Zsolt Kira
How to Train Your DRAGAN. CoRR (2017)
Jacob D. Abernethy, Chansoo Lee, Audra McMillan, Ambuj Tewari
Online Learning via Differential Privacy. CoRR (2017)
Jacob D. Abernethy, Sébastien Lahaie, Matus Telgarsky
Rate of Price Discovery in Iterative Combinatorial Auctions. EC (2016)
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
Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing. CoRR (2016)
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
Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences. CoRR (2016)
Jacob D. Abernethy, Elad Hazan
Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier. ICML (2016)
Mahmoud Azab, Rada Mihalcea, Jacob D. Abernethy
Analysing RateMyProfessors Evaluations Across Institutions, Disciplines, and Cultures: The Tell-Tale Signs of a Good Professor. SocInfo (1) (2016)
Jacob D. Abernethy, Kareem Amin, Ruihao Zhu
Threshold Bandits, With and Without Censored Feedback. NIPS (2016)
Jie Li, Paul Ozog, Jacob D. Abernethy, Ryan M. Eustice, Matthew Johnson-Roberson
Utilizing high-dimensional features for real-time robotic applications: Reducing the curse of dimensionality for recursive Bayesian estimation. IROS (2016)
Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari
Fighting Bandits with a New Kind of Smoothness. CoRR (2015)
Jacob D. Abernethy, Elad Hazan
Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier. CoRR (2015)
Jacob D. Abernethy, Matthew Johnson-Roberson
Financialized methods for market-based multi-sensor fusion. IROS (2015)
Jacob D. Abernethy, Sébastien Lahaie, Matus Telgarsky
Rate of Price Discovery in Iterative Combinatorial Auctions. CoRR (2015)
Bo Waggoner, Rafael M. Frongillo, Jacob D. Abernethy
A Market Framework for Eliciting Private Data. NIPS (2015)
Jacob D. Abernethy, Yiling Chen, Chien-Ju Ho, Bo Waggoner
Low-Cost Learning via Active Data Procurement. EC (2015)
Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari
Fighting Bandits with a New Kind of Smoothness. NIPS (2015)
Jacob D. Abernethy, Yiling Chen, Chien-Ju Ho, Bo Waggoner
Actively Purchasing Data for Learning. CoRR (2015)
Jacob D. Abernethy, Chansoo Lee, Abhinav Sinha, Ambuj Tewari
Online Linear Optimization via Smoothing. CoRR (2014)
Jacob D. Abernethy, Sindhu Kutty, Sébastien Lahaie, Rahul Sami
Information aggregation in exponential family markets. EC (2014)
Jacob D. Abernethy, Sindhu Kutty, Sébastien Lahaie, Rahul Sami
Information Aggregation in Exponential Family Markets. CoRR (2014)
Jacob D. Abernethy, Rafael M. Frongillo, Sindhu Kutty
On risk measures, market making, and exponential families. SIGecom Exch. 13 (2) (2014)
Jacob D. Abernethy, Rafael M. Frongillo, Xiaolong Li, Jennifer Wortman Vaughan
A general volume-parameterized market making framework. EC (2014)
Qingsi Wang, Shang-Pin Sheng, Jacob D. Abernethy, Mingyan Liu
Jamming defense against a resource-replenishing adversary in multi-channel wireless systems. WiOpt (2014)
Jacob D. Abernethy, Chansoo Lee, Abhinav Sinha, Ambuj Tewari
Online Linear Optimization via Smoothing. COLT (2014)
Jacob D. Abernethy, Peter L. Bartlett, Rafael M. Frongillo, Andre Wibisono
How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal. NIPS (2013)
H. Brendan McMahan, Jacob D. Abernethy
Minimax Optimal Algorithms for Unconstrained Linear Optimization. NIPS (2013)
Jacob D. Abernethy, Yiling Chen, Jennifer Wortman Vaughan
Efficient Market Making via Convex Optimization, and a Connection to Online Learning. ACM Trans. Economics and Comput. 1 (2) (2013)
Jacob D. Abernethy, Kareem Amin, Michael J. Kearns, Moez Draief
Large-Scale Bandit Problems and KWIK Learning. ICML (1) (2013)
Jacob D. Abernethy, Satyen Kale
Adaptive Market Making via Online Learning. NIPS (2013)
Jacob D. Abernethy, Rafael M. Frongillo
A Characterization of Scoring Rules for Linear Properties. COLT (2012)
Jacob D. Abernethy, Elad Hazan, Alexander Rakhlin
Interior-Point Methods for Full-Information and Bandit Online Learning. IEEE Trans. Inf. Theory 58 (7) (2012)
Jacob D. Abernethy, Rafael M. Frongillo, Andre Wibisono
Minimax option pricing meets black-scholes in the limit. STOC (2012)
Jacob D. Abernethy, Rafael M. Frongillo, Andre Wibisono
Minimax Option Pricing Meets Black-Scholes in the Limit CoRR (2012)
Jacob D. Abernethy, Yiling Chen, Jennifer Wortman Vaughan
An optimization-based framework for automated market-making. EC (2011)
Jacob D. Abernethy, Peter L. Bartlett, Elad Hazan
Blackwell Approachability and No-Regret Learning are Equivalent. COLT (2011)
Jacob D. Abernethy, Shie Mannor
Does an Efficient Calibrated Forecasting Strategy Exist? COLT (2011)
Jacob D. Abernethy, Rafael M. Frongillo
A Collaborative Mechanism for Crowdsourcing Prediction Problems CoRR (2011)
Jacob D. Abernethy, Rafael M. Frongillo
A Collaborative Mechanism for Crowdsourcing Prediction Problems. NIPS (2011)
Jacob D. Abernethy, Peter L. Bartlett, Niv Buchbinder, Isabelle Stanton
A Regularization Approach to Metrical Task Systems. ALT (2010)
Jacob D. Abernethy, Peter L. Bartlett, Elad Hazan
Blackwell Approachability and Low-Regret Learning are Equivalent CoRR (2010)
Jacob D. Abernethy
Can We Learn to Gamble Efficiently? COLT (2010)
Jacob D. Abernethy, Manfred K. Warmuth
Repeated Games against Budgeted Adversaries. NIPS (2010)
Jacob D. Abernethy, Olivier Chapelle, Carlos Castillo
Graph regularization methods for Web spam detection. Mach. Learn. 81 (2) (2010)
Jacob D. Abernethy, Yiling Chen, Jennifer Wortman Vaughan
An Optimization-Based Framework for Automated Market-Making CoRR (2010)
Jacob D. Abernethy, Manfred K. Warmuth
Minimax Games with Bandits. COLT (2009)

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Publication Activity (10 Years)

Top Topics

Top Venues

Jacob D. Abernethy