2 citations · 5 across the 9 of their papers we have counts for
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stat.ML2022
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits
Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun
In sparse linear bandits, a learning agent sequentially selects an action and receive reward feedback, and the reward function depends linearly on a few coordinates of the covariat…
stat.ML2022
Norm-Agnostic Linear Bandits
Spencer, Gales, Sunder Sethuraman +1
Linear bandits have a wide variety of applications including recommendation systems yet they make one strong assumption: the algorithms must know an upper bound on the norm of…
stat.ML2022
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…