Fixed-Budget Best-Arm Identification in Contextual Bandits: A Static-Adaptive Algorithm

06/09/2021
by   MohammadJavad Azizi, et al.
0

We study the problem of best-arm identification (BAI) in contextual bandits in the fixed-budget setting. We propose a general successive elimination algorithm that proceeds in stages and eliminates a fixed fraction of suboptimal arms in each stage. This design takes advantage of the strengths of static and adaptive allocations. We analyze the algorithm in linear models and obtain a better error bound than prior work. We also apply it to generalized linear models (GLMs) and bound its error. This is the first BAI algorithm for GLMs in the fixed-budget setting. Our extensive numerical experiments show that our algorithm outperforms the state of art.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset