Jacob D. Abernethy

Orcid: 0000-0002-3115-6804

Affiliations:
  • University of Michigan, Department of Electrical Engineering and Computer Science
  • University of Pennsylvania, Computer and Information Science Department


According to our database1, Jacob D. Abernethy authored at least 91 papers between 2006 and 2024.

Collaborative distances:

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
No-regret dynamics in the Fenchel game: a unified framework for algorithmic convex optimization.
Math. Program., May, 2024

2023
Accelerated Federated Optimization with Quantization.
IEEE Data Eng. Bull., 2023

Extragradient Type Methods for Riemannian Variational Inequality Problems.
CoRR, 2023

On the Robustness of Epoch-Greedy in Multi-Agent Contextual Bandit Mechanisms.
CoRR, 2023

Randomized Quantization is All You Need for Differential Privacy in Federated Learning.
CoRR, 2023

A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks.
CoRR, 2023

Faster Margin Maximization Rates for Generic Optimization Methods.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Riemannian Projection-free Online Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On Accelerated Perceptrons and Beyond.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Minimizing Dynamic Regret on Geodesic Metric Spaces.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023


2022
Adaptive Oracle-Efficient Online Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

ActiveHedge: Hedge meets Active Learning.
Proceedings of the International Conference on Machine Learning, 2022

Active Sampling for Min-Max Fairness.
Proceedings of the International Conference on Machine Learning, 2022

2021
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness.
CoRR, 2021

Fast Convergence of Fictitious Play for Diagonal Payoff Matrices.
Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms, 2021

Observation-Free Attacks on Stochastic Bandits.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network.
Proceedings of the 38th International Conference on Machine Learning, 2021

Last-Iterate Convergence Rates for Min-Max Optimization: Convergence of Hamiltonian Gradient Descent and Consensus Optimization.
Proceedings of the Algorithmic Learning Theory, 2021

Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron.
Proceedings of the Asian Conference on Machine Learning, 2021

2020
Linear Separation via Optimism.
CoRR, 2020

Provable Acceleration of Neural Net Training via Polyak's Momentum.
CoRR, 2020

Quickly Finding a Benign Region via Heavy Ball Momentum in Non-Convex Optimization.
CoRR, 2020

Online Kernel based Generative Adversarial Networks.
CoRR, 2020

Adaptive Sampling to Reduce Disparate Performance.
CoRR, 2020

Escaping Saddle Points Faster with Stochastic Momentum.
Proceedings of the 8th International Conference on Learning Representations, 2020

Conference on Learning Theory 2020: Preface.
Proceedings of the Conference on Learning Theory, 2020

2019
Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments.
Mark. Sci., 2019

Fictitious Play: Convergence, Smoothness, and Optimism.
CoRR, 2019

Competing Against Equilibria in Zero-Sum Games with Evolving Payoffs.
CoRR, 2019

Last-iterate convergence rates for min-max optimization.
CoRR, 2019

Online Learning via the Differential Privacy Lens.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning Auctions with Robust Incentive Guarantees.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Acceleration through Optimistic No-Regret Dynamics.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

ActiveRemediation: The Search for Lead Pipes in Flint, Michigan.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Faster Rates for Convex-Concave Games.
Proceedings of the Conference On Learning Theory, 2018

2017
Online Learning via Differential Privacy.
CoRR, 2017

Addendum to "A Market Framework for Eliciting Private Data".
CoRR, 2017

How to Train Your DRAGAN.
CoRR, 2017

On Frank-Wolfe and Equilibrium Computation.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

A Data Science Approach to Understanding Residential Water Contamination in Flint.
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

2016
Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing.
CoRR, 2016

Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences.
CoRR, 2016

Analysing RateMyProfessors Evaluations Across Institutions, Disciplines, and Cultures: The Tell-Tale Signs of a Good Professor.
Proceedings of the Social Informatics - 8th International Conference, 2016

Rate of Price Discovery in Iterative Combinatorial Auctions.
Proceedings of the 2016 ACM Conference on Economics and Computation, 2016

Threshold Bandits, With and Without Censored Feedback.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Utilizing high-dimensional features for real-time robotic applications: Reducing the curse of dimensionality for recursive Bayesian estimation.
Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016

Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Actively Purchasing Data for Learning.
CoRR, 2015

Low-Cost Learning via Active Data Procurement.
Proceedings of the Sixteenth ACM Conference on Economics and Computation, 2015

A Market Framework for Eliciting Private Data.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Fighting Bandits with a New Kind of Smoothness.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Financialized methods for market-based multi-sensor fusion.
Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2015

2014
On risk measures, market making, and exponential families.
SIGecom Exch., 2014

Jamming defense against a resource-replenishing adversary in multi-channel wireless systems.
Proceedings of the 12th International Symposium on Modeling and Optimization in Mobile, 2014

Information aggregation in exponential family markets.
Proceedings of the ACM Conference on Economics and Computation, 2014

A general volume-parameterized market making framework.
Proceedings of the ACM Conference on Economics and Computation, 2014

Online Linear Optimization via Smoothing.
Proceedings of The 27th Conference on Learning Theory, 2014

2013
Efficient Market Making via Convex Optimization, and a Connection to Online Learning.
ACM Trans. Economics and Comput., 2013

Minimax Optimal Algorithms for Unconstrained Linear Optimization.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Adaptive Market Making via Online Learning.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Large-Scale Bandit Problems and KWIK Learning.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
Interior-Point Methods for Full-Information and Bandit Online Learning.
IEEE Trans. Inf. Theory, 2012

A Characterization of Scoring Rules for Linear Properties.
Proceedings of the COLT 2012, 2012

Minimax option pricing meets black-scholes in the limit.
Proceedings of the 44th Symposium on Theory of Computing Conference, 2012

2011
Sequential Decision Making in Non-stochastic Environments.
PhD thesis, 2011

Does an Efficient Calibrated Forecasting Strategy Exist?
Proceedings of the COLT 2011, 2011

Blackwell Approachability and No-Regret Learning are Equivalent.
Proceedings of the COLT 2011, 2011

An optimization-based framework for automated market-making.
Proceedings of the Proceedings 12th ACM Conference on Electronic Commerce (EC-2011), 2011

A Collaborative Mechanism for Crowdsourcing Prediction Problems.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010
Graph regularization methods for Web spam detection.
Mach. Learn., 2010

Blackwell Approachability and Low-Regret Learning are Equivalent
CoRR, 2010

Repeated Games against Budgeted Adversaries.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Can We Learn to Gamble Efficiently?
Proceedings of the COLT 2010, 2010

A Regularization Approach to Metrical Task Systems.
Proceedings of the Algorithmic Learning Theory, 21st International Conference, 2010

2009
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization.
J. Mach. Learn. Res., 2009

Minimax Games with Bandits.
Proceedings of the COLT 2009, 2009

An Efficient Bandit Algorithm for sqrt(T) Regret in Online Multiclass Prediction?.
Proceedings of the COLT 2009, 2009

Beating the Adaptive Bandit with High Probability.
Proceedings of the COLT 2009, 2009

A Stochastic View of Optimal Regret through Minimax Duality.
Proceedings of the COLT 2009, 2009

2008
Eliciting Consumer Preferences Using Robust Adaptive Choice Questionnaires.
IEEE Trans. Knowl. Data Eng., 2008

When Random Play is Optimal Against an Adversary.
Proceedings of the 21st Annual Conference on Learning Theory, 2008

Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization.
Proceedings of the 21st Annual Conference on Learning Theory, 2008

Optimal Stragies and Minimax Lower Bounds for Online Convex Games.
Proceedings of the 21st Annual Conference on Learning Theory, 2008

Web spam identification through content and hyperlinks.
Proceedings of the AIRWeb 2008, 2008

2007
Online discovery of similarity mappings.
Proceedings of the Machine Learning, 2007

Multitask Learning with Expert Advice.
Proceedings of the Learning Theory, 20th Annual Conference on Learning Theory, 2007

2006
Low-rank matrix factorization with attributes
CoRR, 2006

Continuous Experts and the Binning Algorithm.
Proceedings of the Learning Theory, 19th Annual Conference on Learning Theory, 2006


  Loading...