Karthik Sridharan

research

∙ 07/24/2023

Contextual Bandits and Imitation Learning via Preference-Based Active Queries

We consider the problem of contextual bandits and imitation learning, wh...

0 Ayush Sekhari, et al. ∙

research

∙ 07/11/2023

Selective Sampling and Imitation Learning via Online Regression

We consider the problem of Imitation Learning (IL) by actively querying ...

0 Ayush Sekhari, et al. ∙

research

∙ 10/13/2022

From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent

Stochastic Gradient Descent (SGD) has been the method of choice for lear...

0 Satyen Kale, et al. ∙

research

∙ 06/27/2022

On the Complexity of Adversarial Decision Making

A central problem in online learning and decision making – from bandits ...

8 Dylan J. Foster, 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

∙ 07/11/2021

SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs

Multi-epoch, small-batch, Stochastic Gradient Descent (SGD) has been the...

0 Satyen Kale, 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

∙ 12/03/2020

Online learning with dynamics: A minimax perspective

We study the problem of online learning with dynamics, where a learner i...

0 Kush Bhatia, et al. ∙

research

∙ 06/24/2020

Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations

We design an algorithm which finds an ϵ-approximate stationary point (wi...

0 Yossi Arjevani, 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

∙ 04/09/2019

Hypothesis Set Stability and Generalization

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

0 Dylan J. Foster, et al. ∙

research

∙ 02/28/2019

Distributed Learning with Sublinear Communication

In distributed statistical learning, N samples are split across m machin...

0 Jayadev Acharya, et al. ∙

research

∙ 02/13/2019

The Complexity of Making the Gradient Small in Stochastic Convex Optimization

We give nearly matching upper and lower bounds on the oracle complexity ...

0 Dylan Foster, et al. ∙

research

∙ 10/25/2018

Uniform Convergence of Gradients for Non-Convex Learning and Optimization

We investigate 1) the rate at which refined properties of the empirical ...

0 Dylan J. Foster, et al. ∙

research

∙ 09/11/2018

Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals

We show that many machine learning goals, such as improved fairness metr...

0 Andrew Cotter, et al. ∙

research

∙ 06/29/2018

Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints

Classifiers can be trained with data-dependent constraints to satisfy fa...

0 Andrew Cotter, et al. ∙

research

∙ 04/17/2018

Two-Player Games for Efficient Non-Convex Constrained Optimization

In recent years, constrained optimization has become increasingly releva...

0 Andrew Cotter, 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

∙ 03/20/2018

Online Learning: Sufficient Statistics and the Burkholder Method

We uncover a fairly general principle in online learning: If regret can ...

0 Dylan J. Foster, et al. ∙

research

∙ 11/09/2017

Small-loss bounds for online learning with partial information

We consider the problem of adversarial (non-stochastic) online learning ...

0 Thodoris Lykouris, et al. ∙

research

∙ 04/13/2017

ZigZag: A new approach to adaptive online learning

We develop a novel family of algorithms for the online learning setting ...

0 Dylan J. Foster, et al. ∙

research

∙ 08/31/2016

A Tutorial on Online Supervised Learning with Applications to Node Classification in Social Networks

We revisit the elegant observation of T. Cover '65 which, perhaps, is no...

0 Alexander Rakhlin, et al. ∙

research

∙ 02/06/2016

BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits

We present efficient algorithms for the problem of contextual bandits wi...

0 Alexander Rakhlin, et al. ∙

research

∙ 02/05/2016

Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters

The amount of data available in the world is growing faster than our abi...

0 Zeyuan Allen-Zhu, et al. ∙

research

∙ 12/17/2015

Private Causal Inference

Causal inference deals with identifying which random variables "cause" o...

0 Matt J. Kusner, et al. ∙

research

∙ 10/13/2015

On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities

We study an equivalence of (i) deterministic pathwise statements appeari...

0 Alexander Rakhlin, et al. ∙

research

∙ 08/21/2015

Adaptive Online Learning

We propose a general framework for studying adaptive regret bounds in th...

0 Dylan J. Foster, et al. ∙

research

∙ 03/04/2015

Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints

We study online prediction where regret of the algorithm is measured aga...

0 Alexander Rakhlin, et al. ∙

research

∙ 02/21/2015

Learning with Square Loss: Localization through Offset Rademacher Complexity

We consider regression with square loss and general classes of functions...

0 Tengyuan Liang, et al. ∙

research

∙ 01/29/2015

Sequential Probability Assignment with Binary Alphabets and Large Classes of Experts

We analyze the problem of sequential probability assignment for binary o...

0 Alexander Rakhlin, et al. ∙

research

∙ 01/26/2015

Online Nonparametric Regression with General Loss Functions

This paper establishes minimax rates for online regression with arbitrar...

0 Alexander Rakhlin, et al. ∙

research

∙ 01/26/2015

Online Optimization : Competing with Dynamic Comparators

Recent literature on online learning has focused on developing adaptive ...

0 Ali Jadbabaie, et al. ∙

research

∙ 02/11/2014

Online Nonparametric Regression

We establish optimal rates for online regression for arbitrary classes o...

0 Alexander Rakhlin, et al. ∙

research

∙ 02/12/2013

Competing With Strategies

We study the problem of online learning with a notion of regret defined ...

0 Wei Han, et al. ∙

research

∙ 08/18/2012

Online Learning with Predictable Sequences

We present methods for online linear optimization that take advantage of...

0 Alexander Rakhlin, et al. ∙

research

∙ 06/27/2012

Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss

We carefully study how well minimizing convex surrogate loss functions, ...

0 Shai Ben-David, et al. ∙

research

∙ 04/04/2012

Relax and Localize: From Value to Algorithms

We show a principled way of deriving online learning algorithms from a m...

0 Alexander Rakhlin, et al. ∙

research

∙ 04/27/2011

Online Learning: Stochastic and Constrained Adversaries

Learning theory has largely focused on two main learning scenarios. The ...

0 Alexander Rakhlin, et al. ∙

research

∙ 11/14/2010

Online Learning: Beyond Regret

We study online learnability of a wide class of problems, extending the ...

0 Alexander Rakhlin, et al. ∙

research

∙ 10/31/2009

Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity

The versatility of exponential families, along with their attendant conv...

0 Sham M. Kakade, et al. ∙

Karthik Sridharan

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