2 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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,…