b'Mehryar Mohri'

research

∙ 07/05/2023

Ranking with Abstention

We introduce a novel framework of ranking with abstention, where the lea...

0 Anqi Mao, et al. ∙

research

∙ 06/15/2023

Differentially Private Domain Adaptation with Theoretical Guarantees

In many applications, the labeled data at the learner's disposal is subj...

0 Raef Bassily, et al. ∙

research

∙ 05/10/2023

Best-Effort Adaptation

We study a problem of best-effort adaptation motivated by several applic...

0 Pranjal Awasthi, et al. ∙

research

∙ 04/14/2023

Cross-Entropy Loss Functions: Theoretical Analysis and Applications

Cross-entropy is a widely used loss function in applications. It coincid...

0 Anqi Mao, et al. ∙

research

∙ 02/03/2023

Pseudonorm Approachability and Applications to Regret Minimization

Blackwell's celebrated approachability theory provides a general framewo...

0 Christoph Dann, et al. ∙

research

∙ 08/12/2022

Private Domain Adaptation from a Public Source

A key problem in a variety of applications is that of domain adaptation ...

1 Raef Bassily, et al. ∙

research

∙ 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...

0 Teodor V. Marinov, et al. ∙

research

∙ 06/19/2022

Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation

Myopic exploration policies such as epsilon-greedy, softmax, or Gaussian...

0 Christoph Dann, et al. ∙

research

∙ 05/17/2022

Strategizing against Learners in Bayesian Games

We study repeated two-player games where one of the players, the learner...

6 Yishay Mansour, et al. ∙

research

∙ 05/16/2022

ℋ-Consistency Estimation Error of Surrogate Loss Minimizers

We present a detailed study of estimation errors in terms of surrogate l...

5 Pranjal Awasthi, et al. ∙

research

∙ 04/21/2022

Differentially Private Learning with Margin Guarantees

We present a series of new differentially private (DP) algorithms with d...

0 Raef Bassily, et al. ∙

research

∙ 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...

12 Pranjal Awasthi, et al. ∙

research

∙ 07/12/2021

Adapting to Misspecification in Contextual Bandits

A major research direction in contextual bandits is to develop algorithm...

7 Dylan J. Foster, et al. ∙

research

∙ 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...

6 Christoph Dann, et al. ∙

research

∙ 06/22/2021

Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations

There have been many recent advances on provably efficient Reinforcement...

10 Christoph Dann, et al. ∙

research

∙ 05/04/2021

A Finer Calibration Analysis for Adversarial Robustness

We present a more general analysis of H-calibration for adversarially ro...

15 Pranjal Awasthi, et al. ∙

research

∙ 04/19/2021

Calibration and Consistency of Adversarial Surrogate Losses

Adversarial robustness is an increasingly critical property of classifie...

0 Pranjal Awasthi, et al. ∙

research

∙ 04/06/2021

Communication-Efficient Agnostic Federated Averaging

In distributed learning settings such as federated learning, the trainin...

5 Jae Ro, et al. ∙

research

∙ 02/23/2021

Learning with User-Level Privacy

We propose and analyze algorithms to solve a range of learning tasks und...

5 Daniel Lévy, et al. ∙

research

∙ 08/25/2020

Multiple-Source Adaptation with Domain Classifiers

We consider the multiple-source adaptation (MSA) problem and improve a p...

12 Corinna Cortes, et al. ∙

research

∙ 08/21/2020

Beyond Individual and Group Fairness

We present a new data-driven model of fairness that, unlike existing sta...

8 Pranjal Awasthi, et al. ∙

research

∙ 08/08/2020

Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Federated learning is a challenging optimization problem due to the hete...

0 Sai Praneeth Karimireddy, et al. ∙

research

∙ 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...

0 Pranjal Awasthi, et al. ∙

research

∙ 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 ...

0 Yishay Mansour, et al. ∙

research

∙ 06/26/2020

Relative Deviation Margin Bounds

We present a series of new and more favorable margin-based learning guar...

7 Corinna Cortes, et al. ∙

research

∙ 06/16/2020

Corralling Stochastic Bandit Algorithms

We study the problem of corralling stochastic bandit algorithms, that is...

0 Raman Arora, et al. ∙

research

∙ 05/07/2020

Reinforcement Learning with Feedback Graphs

We study episodic reinforcement learning in Markov decision processes wh...

28 Christoph Dann, et al. ∙

research

∙ 04/28/2020

Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks

Adversarial or test time robustness measures the susceptibility of a cla...

5 Pranjal Awasthi, et al. ∙

research

∙ 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...

32 Yishay Mansour, et al. ∙

research

∙ 02/18/2020

Adaptive Region-Based Active Learning

We present a new active learning algorithm that adaptively partitions th...

2 Corinna Cortes, et al. ∙

research

∙ 12/10/2019

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...

33 Peter Kairouz, et al. ∙

research

∙ 10/20/2019

Learning GANs and Ensembles Using Discrepancy

Generative adversarial networks (GANs) generate data based on minimizing...

30 Ben Adlam, et al. ∙

research

∙ 10/14/2019

SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning

Federated learning is a key scenario in modern large-scale machine learn...

7 Sai Praneeth Karimireddy, et al. ∙

research

∙ 07/29/2019

Bandits with Feedback Graphs and Switching Costs

We study the adversarial multi-armed bandit problem where partial observ...

4 Raman Arora, et al. ∙

research

∙ 04/30/2019

AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles

AdaNet is a lightweight TensorFlow-based (Abadi et al., 2015) framework ...

38 Charles Weill, et al. ∙

research

∙ 04/09/2019

Hypothesis Set Stability and Generalization

We present an extensive study of generalization for data-dependent hypot...

0 Dylan J. Foster, et al. ∙

research

∙ 02/01/2019

Agnostic Federated Learning

A key learning scenario in large-scale applications is that of federated...

16 Mehryar Mohri, et al. ∙

research

∙ 11/09/2018

Policy Regret in Repeated Games

The notion of policy regret in online learning is a well defined? perfor...

0 Raman Arora, et al. ∙

research

∙ 05/20/2018

Algorithms and Theory for Multiple-Source Adaptation

This work includes a number of novel contributions for the multiple-sour...

0 Judy Hoffman, et al. ∙

research

∙ 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...

0 Corinna Cortes, et al. ∙

research

∙ 03/25/2018

Logistic Regression: The Importance of Being Improper

Learning linear predictors with the logistic loss---both in stochastic a...

0 Dylan J. Foster, et al. ∙

research

∙ 03/15/2018

Theory and Algorithms for Forecasting Time Series

We present data-dependent learning bounds for the general scenario of no...

0 Vitaly Kuznetsov, et al. ∙

research

∙ 12/30/2017

Parameter-free online learning via model selection

We introduce an efficient algorithmic framework for model selection in o...

0 Dylan J. Foster, et al. ∙

research

∙ 05/20/2016

Structured Prediction Theory Based on Factor Graph Complexity

We present a general theoretical analysis of structured prediction with ...

0 Corinna Cortes, et al. ∙

research

∙ 09/29/2015

Foundations of Coupled Nonlinear Dimensionality Reduction

In this paper we introduce and analyze the learning scenario of coupled ...

0 Mehryar Mohri, et al. ∙

research

∙ 09/18/2015

Accelerating Optimization via Adaptive Prediction

We present a powerful general framework for designing data-dependent opt...

0 Mehryar Mohri, et al. ∙

research

∙ 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...

0 Spencer Greenberg, et al. ∙

research

∙ 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...

0 Mehryar Mohri, et al. ∙

research

∙ 05/09/2012

Multiple Source Adaptation and the Renyi Divergence

This paper presents a novel theoretical study of the general problem of ...

0 Yishay Mansour, et al. ∙

research

∙ 05/09/2012

L2 Regularization for Learning Kernels

The choice of the kernel is critical to the success of many learning alg...

0 Corinna Cortes, et al. ∙

Mehryar Mohri

Featured Co-authors