331 citations · 418 across the 18 of their papers we have counts for
9 papers · 1 filter
A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity
Zhihan Xiong, Romain Camilleri, Maryam Fazel +2
We investigate the fixed-budget best-arm identification (BAI) problem for linear bandits in a potentially non-stationary environment. Given a finite arm set $\mathcal{X}\subset\mat…
Adaptive Experimental Design and Counterfactual Inference
Tanner Fiez, Sergio Gamez, Arick Chen +2
Adaptive experimental design methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/N test…
Active Learning with Safety Constraints
Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern +2
Active learning methods have shown great promise in reducing the number of samples necessary for learning. As automated learning systems are adopted into real-time, real-world deci…
Selective Sampling for Online Best-arm Identification
Romain Camilleri, Zhihan Xiong, Maryam Fazel +2
This work considers the problem of selective-sampling for best-arm identification. Given a set of potential options , a learner aims to compute with…
Improved Algorithms for Agnostic Pool-based Active Classification
Julian Katz-Samuels, Jifan Zhang, Lalit Jain +1
We consider active learning for binary classification in the agnostic pool-based setting. The vast majority of works in active learning in the agnostic setting are inspired by the…
Spectral Methods for Ranking with Scarce Data
Umang Varma, Lalit Jain, Anna C. Gilbert
Given a number of pairwise preferences of items, a common task is to rank all the items. Examples include pairwise movie ratings, New Yorker cartoon caption contests, and many othe…
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits
Julian Katz-Samuels, Lalit Jain, Zohar Karnin +1
This paper proposes near-optimal algorithms for the pure-exploration linear bandit problem in the fixed confidence and fixed budget settings. Leveraging ideas from the theory of su…
Adaptive Sampling for Coarse Ranking
Sumeet Katariya, Lalit Jain, Nandana Sengupta +2
We consider the problem of active coarse ranking, where the goal is to sort items according to their means into clusters of pre-specified sizes, by adaptively sampling from their r…
The Extreme Value Machine
Ethan M. Rudd, Lalit P. Jain, Walter J. Scheirer +1
It is often desirable to be able to recognize when inputs to a recognition function learned in a supervised manner correspond to classes unseen at training time. With this ability,…