Lalit Jain | DeepAI

AI Chat

Featured Co-authors

Robert Nowak
54 publications
Kevin Jamieson
50 publications
Maryam Fazel
32 publications
Jamie Morgenstern
27 publications
Kwang-Sung Jun
21 publications
Zohar Karnin
21 publications
Sumeet Katariya
19 publications
James Evans
17 publications
Lillian Ratliff
14 publications
Tanner Fiez
13 publications
Andrew Wagenmaker
12 publications

∙ 07/27/2023

A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity

We investigate the fixed-budget best-arm identification (BAI) problem fo...

0 Zhihan Xiong, et al. ∙

∙ 10/25/2022

Adaptive Experimental Design and Counterfactual Inference

Adaptive experimental design methods are increasingly being used in indu...

0 Tanner Fiez, et al. ∙

∙ 07/05/2022

Instance-optimal PAC Algorithms for Contextual Bandits

In the stochastic contextual bandit setting, regret-minimizing algorithm...

0 Zhaoqi Li, et al. ∙

∙ 06/22/2022

Active Learning with Safety Constraints

Active learning methods have shown great promise in reducing the number ...

0 Romain Camilleri, et al. ∙

∙ 02/04/2022

An Experimental Design Approach for Regret Minimization in Logistic Bandits

In this work we consider the problem of regret minimization for logistic...

5 Blake Mason, et al. ∙

∙ 11/02/2021

Nearly Optimal Algorithms for Level Set Estimation

The level set estimation problem seeks to find all points in a domain X ...

0 Blake Mason, et al. ∙

∙ 10/28/2021

Selective Sampling for Online Best-arm Identification

This work considers the problem of selective-sampling for best-arm ident...

0 Romain Camilleri, et al. ∙

∙ 05/13/2021

Improved Algorithms for Agnostic Pool-based Active Classification

We consider active learning for binary classification in the agnostic po...

0 Julian Katz-Samuels, et al. ∙

∙ 11/23/2020

Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits

We propose improved fixed-design confidence bounds for the linear logist...

17 Kwang-Sung Jun, et al. ∙

∙ 08/14/2020

A New Perspective on Pool-Based Active Classification and False-Discovery Control

In many scientific settings there is a need for adaptive experimental de...

0 Lalit Jain, et al. ∙

∙ 07/02/2020

Spectral Methods for Ranking with Scarce Data

Given a number of pairwise preferences of items, a common task is to ran...

0 Umang Varma, et al. ∙

∙ 06/21/2020

An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits

This paper proposes near-optimal algorithms for the pure-exploration lin...

0 Julian Katz-Samuels, et al. ∙

∙ 06/16/2020

Finding All ε-Good Arms in Stochastic Bandits

The pure-exploration problem in stochastic multi-armed bandits aims to f...

0 Blake Mason, et al. ∙

∙ 06/20/2019

Sequential Experimental Design for Transductive Linear Bandits

In this paper we introduce the transductive linear bandit problem: given...

0 Tanner Fiez, et al. ∙

∙ 04/30/2019

Convergence rates for ordinal embedding

We prove optimal bounds for the convergence rate of ordinal embedding (a...

0 Jordan S. Ellenberg, et al. ∙

∙ 09/06/2018

A Bandit Approach to Multiple Testing with False Discovery Control

We propose an adaptive sampling approach for multiple testing which aims...

0 Kevin Jamieson, et al. ∙

∙ 02/20/2018

Adaptive Sampling for Coarse Ranking

We consider the problem of active coarse ranking, where the goal is to s...

0 Sumeet Katariya, et al. ∙

∙ 10/29/2017

If it ain't broke, don't fix it: Sparse metric repair

Many modern data-intensive computational problems either require, or ben...

0 Anna C. Gilbert, et al. ∙

∙ 09/18/2017

Learning Low-Dimensional Metrics

This paper investigates the theoretical foundations of metric learning, ...

0 Lalit Jain, et al. ∙

∙ 06/22/2016

Finite Sample Prediction and Recovery Bounds for Ordinal Embedding

The goal of ordinal embedding is to represent items as points in a low-d...

0 Lalit Jain, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world