2 citations · 3 across the 7 of their papers we have counts for
7 papers
Stochastic Linear Bandits with Partially Observed Actions
Gautam Dasarathy, Vineet Gattani, Lalit Jain
The stochastic linear bandit, where actions are represented as vectors and rewards are linear, is a central paradigm for sequential decision making. We study a partially observed v…
Adaptive Experimentation When You Can't Experiment
Yao Zhao, Kwang-Sung Jun, Tanner Fiez +1
This paper introduces the \emph{confounded pure exploration transductive linear bandit} (\texttt{CPET-LB}) problem. As a motivating example, often online services cannot directly a…
Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice
Tanner Fiez, Houssam Nassif, Yu-Cheng Chen +2
Adaptive experimental design (AED) methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/…
Fair Active Learning in Low-Data Regimes
Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern +2
In critical machine learning applications, ensuring fairness is essential to avoid perpetuating social inequities. In this work, we address the challenges of reducing bias and impr…
Optimal Exploration is no harder than Thompson Sampling
Zhaoqi Li, Kevin Jamieson, Lalit Jain
Given a set of arms and an unknown parameter vector , the pure exploration linear bandit problem aims to return $\arg\max_{…
Experimental Designs for Heteroskedastic Variance
Justin Weltz, Tanner Fiez, Alexander Volfovsky +4
Most linear experimental design problems assume homogeneous variance although heteroskedastic noise is present in many realistic settings. Let a learner have access to a finite set…
Nearly Minimax Optimal Submodular Maximization with Bandit Feedback
Artin Tajdini, Lalit Jain, Kevin Jamieson
We consider maximizing an unknown monotonic, submodular set function with cardinality constraint under stochastic bandit feedback. At each time $t=1,…