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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...
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10/25/2022
Adaptive Experimental Design and Counterfactual Inference
Adaptive experimental design methods are increasingly being used in indu...
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07/05/2022
Instance-optimal PAC Algorithms for Contextual Bandits
In the stochastic contextual bandit setting, regret-minimizing algorithm...
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06/22/2022
Active Learning with Safety Constraints
Active learning methods have shown great promise in reducing the number ...
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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...
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11/02/2021
Nearly Optimal Algorithms for Level Set Estimation
The level set estimation problem seeks to find all points in a domain X ...
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10/28/2021
Selective Sampling for Online Best-arm Identification
This work considers the problem of selective-sampling for best-arm ident...
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05/13/2021
Improved Algorithms for Agnostic Pool-based Active Classification
We consider active learning for binary classification in the agnostic po...
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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...
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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...
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07/02/2020
Spectral Methods for Ranking with Scarce Data
Given a number of pairwise preferences of items, a common task is to ran...
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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...
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06/16/2020
Finding All ε-Good Arms in Stochastic Bandits
The pure-exploration problem in stochastic multi-armed bandits aims to f...
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06/20/2019
Sequential Experimental Design for Transductive Linear Bandits
In this paper we introduce the transductive linear bandit problem: given...
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04/30/2019
Convergence rates for ordinal embedding
We prove optimal bounds for the convergence rate of ordinal embedding (a...
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09/06/2018
A Bandit Approach to Multiple Testing with False Discovery Control
We propose an adaptive sampling approach for multiple testing which aims...
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02/20/2018
Adaptive Sampling for Coarse Ranking
We consider the problem of active coarse ranking, where the goal is to s...
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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...
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09/18/2017
Learning Low-Dimensional Metrics
This paper investigates the theoretical foundations of metric learning, ...
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06/22/2016