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07/04/2023
Generalization Guarantees via Algorithm-dependent Rademacher Complexity
Algorithm- and data-dependent generalization bounds are required to expl...
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06/01/2023
The Risks of Recourse in Binary Classification
Algorithmic recourse provides explanations that help users overturn an u...
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05/01/2023
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits
We consider the adversarial linear contextual bandit setting, which allo...
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04/25/2023
Towards Characterizing the First-order Query Complexity of Learning (Approximate) Nash Equilibria in Zero-sum Matrix Games
In the first-order query model for zero-sum K× K matrix games, playersob...
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03/06/2023
Accelerated Rates between Stochastic and Adversarial Online Convex Optimization
Stochastic and adversarial data are two widely studied settings in onlin...
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02/16/2023
Adaptive Selective Sampling for Online Prediction with Experts
We consider online prediction of a binary sequence with expert advice. F...
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09/14/2022
Modifying Squint for Prediction with Expert Advice in a Changing Environment
We provide a new method for online learning, specifically prediction wit...
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05/31/2022
Attribution-based Explanations that Provide Recourse Cannot be Robust
Different users of machine learning methods require different explanatio...
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02/15/2022
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness
Stochastic and adversarial data are two widely studied settings in onlin...
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02/11/2022
Scale-free Unconstrained Online Learning for Curved Losses
A sequence of works in unconstrained online convex optimisation have inv...
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07/05/2021
Robust Online Convex Optimization in the Presence of Outliers
We consider online convex optimization when a number k of data points ar...
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02/15/2021
Distributed Online Learning for Joint Regret with Communication Constraints
In this paper we consider a distributed online learning setting for jo...
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02/12/2021
MetaGrad: Adaptation using Multiple Learning Rates in Online Learning
We provide a new adaptive method for online convex optimization, MetaGra...
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06/14/2020
Explaining Predictions by Approximating the Local Decision Boundary
Constructing accurate model-agnostic explanations for opaque machine lea...
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02/27/2019
Lipschitz Adaptivity with Multiple Learning Rates in Online Learning
We aim to design adaptive online learning algorithms that take advantage...
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10/25/2018
Fast Exact Bayesian Inference for Sparse Signals in the Normal Sequence Model
We consider exact algorithms for Bayesian inference with model selection...
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02/21/2018
The Many Faces of Exponential Weights in Online Learning
A standard introduction to online learning might place Online Gradient D...
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07/09/2015
Fast rates in statistical and online learning
The speed with which a learning algorithm converges as it is presented w...
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02/27/2015
Second-order Quantile Methods for Experts and Combinatorial Games
We aim to design strategies for sequential decision making that adjust t...
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05/07/2014
PAC-Bayes Mini-tutorial: A Continuous Union Bound
When I first encountered PAC-Bayesian concentration inequalities they se...
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02/10/2014
A Second-order Bound with Excess Losses
We study online aggregation of the predictions of experts, and first sho...
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01/03/2013
Follow the Leader If You Can, Hedge If You Must
Follow-the-Leader (FTL) is an intuitive sequential prediction strategy t...
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10/28/2011
Adaptive Hedge
Most methods for decision-theoretic online learning are based on the Hed...
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07/07/2008
