1.1k citations · 1.9k across the 55 of their papers we have counts for
14 papers · 1 filter
Instance-optimal PAC Algorithms for Contextual Bandits
Zhaoqi Li, Lillian Ratliff, Houssam Nassif +2
In the stochastic contextual bandit setting, regret-minimizing algorithms have been extensively researched, but their instance-minimizing best-arm identification counterparts remai…
Nearly Optimal Algorithms for Level Set Estimation
Blake Mason, Romain Camilleri, Subhojyoti Mukherjee +3
The level set estimation problem seeks to find all points in a domain where the value of an unknown function exceeds a threshold .…
A New Perspective on Pool-Based Active Classification and False-Discovery Control
Lalit Jain, Kevin Jamieson
In many scientific settings there is a need for adaptive experimental design to guide the process of identifying regions of the search space that contain as many true positives as…
Estimating the number and effect sizes of non-null hypotheses
Jennifer Brennan, Ramya Korlakai Vinayak, Kevin Jamieson
We study the problem of estimating the distribution of effect sizes (the mean of the test statistic under the alternate hypothesis) in a multiple testing setting. Knowing this dist…
Sequential Experimental Design for Transductive Linear Bandits
Tanner Fiez, Lalit Jain, Kevin Jamieson +1
In this paper we introduce the transductive linear bandit problem: given a set of measurement vectors , a set of items $\mathcal{Z}\subset \mathbb{…
The True Sample Complexity of Identifying Good Arms
Julian Katz-Samuels, Kevin Jamieson
We consider two multi-armed bandit problems with arms: (i) given an , identify an arm with mean that is within of the largest mean and (ii) given a threshold an…
Pure-Exploration for Infinite-Armed Bandits with General Arm Reservoirs
Maryam Aziz, Kevin Jamieson, Javed Aslam
This paper considers a multi-armed bandit game where the number of arms is much larger than the maximum budget and is effectively infinite. We characterize necessary and sufficient…
A Bandit Approach to Multiple Testing with False Discovery Control
Kevin Jamieson, Lalit Jain
We propose an adaptive sampling approach for multiple testing which aims to maximize statistical power while ensuring anytime false discovery control. We consider distributions…
A framework for Multi-A(rmed)/B(andit) testing with online FDR control
Fanny Yang, Aaditya Ramdas, Kevin Jamieson +1
We propose an alternative framework to existing setups for controlling false alarms when multiple A/B tests are run over time. This setup arises in many practical applications, e.g…
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding
Lalit Jain, Kevin Jamieson, Robert Nowak
The goal of ordinal embedding is to represent items as points in a low-dimensional Euclidean space given a set of constraints in the form of distance comparisons like "item is…
Sparse Dueling Bandits
Kevin Jamieson, Sumeet Katariya, Atul Deshpande +1
The dueling bandit problem is a variation of the classical multi-armed bandit in which the allowable actions are noisy comparisons between pairs of arms. This paper focuses on a ne…
lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits
Kevin Jamieson, Matthew Malloy, Robert Nowak +1
The paper proposes a novel upper confidence bound (UCB) procedure for identifying the arm with the largest mean in a multi-armed bandit game in the fixed confidence setting using a…
On Finding the Largest Mean Among Many
Kevin Jamieson, Matthew Malloy, Robert Nowak +1
Sampling from distributions to find the one with the largest mean arises in a broad range of applications, and it can be mathematically modeled as a multi-armed bandit problem in w…
Query Complexity of Derivative-Free Optimization
Kevin G. Jamieson, Robert D. Nowak, Benjamin Recht
This paper provides lower bounds on the convergence rate of Derivative Free Optimization (DFO) with noisy function evaluations, exposing a fundamental and unavoidable gap between t…