22 citations · 55 across the 9 of their papers we have counts for
7 papers · 1 filter
An Experimental Design Approach for Regret Minimization in Logistic Bandits
Blake Mason, Kwang-Sung Jun, Lalit Jain
In this work we consider the problem of regret minimization for logistic bandits. The main challenge of logistic bandits is reducing the dependence on a potentially large problem d…
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…
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{…
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…
If it ain't broke, don't fix it: Sparse metric repair
Anna C. Gilbert, Lalit Jain
Many modern data-intensive computational problems either require, or benefit from distance or similarity data that adhere to a metric. The algorithms run faster or have better perf…