New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem

arXiv:2312.12400v2 Announce Type: replace
Abstract: We consider the sparse contextual bandit problem where arm feature affects reward through the inner product of sparse parameters. Recent studies have developed sparsity-agnostic algorithms based on the greedy arm selection policy. However, the analysis of these algorithms requires strong assumptions on the arm feature distribution to ensure that the greedily selected samples are sufficiently diverse; One of the most common assumptions, relaxed symmetry, imposes approximate origin-symmetry on the distribution, which cannot allow distributions that …

abstract algorithms analysis arm arxiv assumptions cs.lg feature however linear parameters policy product sparsity stat.ml studies through type