Accelerating Linear Bandits with Persistent Candidate Sets
Exhaustive optimistic scoring can make action selection costly in linear bandits with a large fixed set of $K$ actions, requiring $\mathcal{O}(K)$ score evaluations per round. We introduce Persistent Candidate LinUCB (\textsc{PC-LinUCB}), which maintains $k$ active actions and samples $q$ challengers uniformly from the...
Bing-Kui Tong, Daria Korovaitceva, M. Sheshukova et al.
· 0 citations