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07/05/2023
Ranking with Abstention
We introduce a novel framework of ranking with abstention, where the lea...
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06/15/2023
Differentially Private Domain Adaptation with Theoretical Guarantees
In many applications, the labeled data at the learner's disposal is subj...
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05/10/2023
Best-Effort Adaptation
We study a problem of best-effort adaptation motivated by several applic...
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04/14/2023
Cross-Entropy Loss Functions: Theoretical Analysis and Applications
Cross-entropy is a widely used loss function in applications. It coincid...
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02/03/2023
Pseudonorm Approachability and Applications to Regret Minimization
Blackwell's celebrated approachability theory provides a general framewo...
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08/12/2022
Private Domain Adaptation from a Public Source
A key problem in a variety of applications is that of domain adaptation ...
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06/20/2022
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality
We revisit the problem of stochastic online learning with feedback graph...
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06/19/2022
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation
Myopic exploration policies such as epsilon-greedy, softmax, or Gaussian...
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05/17/2022
Strategizing against Learners in Bayesian Games
We study repeated two-player games where one of the players, the learner...
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05/16/2022
â„‹-Consistency Estimation Error of Surrogate Loss Minimizers
We present a detailed study of estimation errors in terms of surrogate l...
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04/21/2022
Differentially Private Learning with Margin Guarantees
We present a series of new differentially private (DP) algorithms with d...
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12/03/2021
On the Existence of the Adversarial Bayes Classifier (Extended Version)
Adversarial robustness is a critical property in a variety of modern mac...
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07/12/2021
Adapting to Misspecification in Contextual Bandits
A major research direction in contextual bandits is to develop algorithm...
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07/02/2021
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning
We provide improved gap-dependent regret bounds for reinforcement learni...
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06/22/2021
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations
There have been many recent advances on provably efficient Reinforcement...
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05/04/2021
A Finer Calibration Analysis for Adversarial Robustness
We present a more general analysis of H-calibration for adversarially ro...
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04/19/2021
Calibration and Consistency of Adversarial Surrogate Losses
Adversarial robustness is an increasingly critical property of classifie...
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04/06/2021
Communication-Efficient Agnostic Federated Averaging
In distributed learning settings such as federated learning, the trainin...
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02/23/2021
Learning with User-Level Privacy
We propose and analyze algorithms to solve a range of learning tasks und...
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08/25/2020
Multiple-Source Adaptation with Domain Classifiers
We consider the multiple-source adaptation (MSA) problem and improve a p...
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08/21/2020
Beyond Individual and Group Fairness
We present a new data-driven model of fairness that, unlike existing sta...
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08/08/2020
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
Federated learning is a challenging optimization problem due to the hete...
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07/21/2020
On the Rademacher Complexity of Linear Hypothesis Sets
Linear predictors form a rich class of hypotheses used in a variety of l...
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07/19/2020
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data
We study multiple-source domain adaptation, when the learner has access ...
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06/26/2020
Relative Deviation Margin Bounds
We present a series of new and more favorable margin-based learning guar...
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06/16/2020
Corralling Stochastic Bandit Algorithms
We study the problem of corralling stochastic bandit algorithms, that is...
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05/07/2020
Reinforcement Learning with Feedback Graphs
We study episodic reinforcement learning in Markov decision processes wh...
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04/28/2020
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
Adversarial or test time robustness measures the susceptibility of a cla...
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02/25/2020
Three Approaches for Personalization with Applications to Federated Learning
The standard objective in machine learning is to train a single model fo...
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02/18/2020
Adaptive Region-Based Active Learning
We present a new active learning algorithm that adaptively partitions th...
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12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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10/20/2019
Learning GANs and Ensembles Using Discrepancy
Generative adversarial networks (GANs) generate data based on minimizing...
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10/14/2019
SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning
Federated learning is a key scenario in modern large-scale machine learn...
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07/29/2019
Bandits with Feedback Graphs and Switching Costs
We study the adversarial multi-armed bandit problem where partial observ...
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04/30/2019
AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles
AdaNet is a lightweight TensorFlow-based (Abadi et al., 2015) framework ...
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04/09/2019
Hypothesis Set Stability and Generalization
We present an extensive study of generalization for data-dependent hypot...
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02/01/2019
Agnostic Federated Learning
A key learning scenario in large-scale applications is that of federated...
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11/09/2018
Policy Regret in Repeated Games
The notion of policy regret in online learning is a well defined? perfor...
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05/20/2018
Algorithms and Theory for Multiple-Source Adaptation
This work includes a number of novel contributions for the multiple-sour...
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04/18/2018
Online Non-Additive Path Learning under Full and Partial Information
We consider the online path learning problem in a graph with non-additiv...
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03/25/2018
Logistic Regression: The Importance of Being Improper
Learning linear predictors with the logistic loss---both in stochastic a...
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03/15/2018
Theory and Algorithms for Forecasting Time Series
We present data-dependent learning bounds for the general scenario of no...
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12/30/2017
Parameter-free online learning via model selection
We introduce an efficient algorithmic framework for model selection in o...
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05/20/2016
Structured Prediction Theory Based on Factor Graph Complexity
We present a general theoretical analysis of structured prediction with ...
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09/29/2015
Foundations of Coupled Nonlinear Dimensionality Reduction
In this paper we introduce and analyze the learning scenario of coupled ...
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09/18/2015
Accelerating Optimization via Adaptive Prediction
We present a powerful general framework for designing data-dependent opt...
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06/06/2013
Tight Lower Bound on the Probability of a Binomial Exceeding its Expectation
We give the proof of a tight lower bound on the probability that a binom...
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05/19/2012
New Analysis and Algorithm for Learning with Drifting Distributions
We present a new analysis of the problem of learning with drifting distr...
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05/09/2012
Multiple Source Adaptation and the Renyi Divergence
This paper presents a novel theoretical study of the general problem of ...
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05/09/2012
