20 citations · 27 across the 4 of their papers we have counts for
7 papers
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers
Julian Katz-Samuels, Blake Mason, Kevin Jamieson +1
We consider interactive learning in the realizable setting and develop a general framework to handle problems ranging from best arm identification to active classification. We begi…
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…
High-Dimensional Experimental Design and Kernel Bandits
Romain Camilleri, Julian Katz-Samuels, Kevin Jamieson
In recent years methods from optimal linear experimental design have been leveraged to obtain state of the art results for linear bandits. A design returned from an objective such…
Experimental Design for Regret Minimization in Linear Bandits
Andrew Wagenmaker, Julian Katz-Samuels, Kevin Jamieson
In this paper we propose a novel experimental design-based algorithm to minimize regret in online stochastic linear and combinatorial bandits. While existing literature tends to fo…
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…
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…