331 citations · 418 across the 18 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
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…