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On the Minimax Regret for Linear Bandits in a wide variety of Action Spaces

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arxiv 2301.03597 v1 pith:ATAMAHPW submitted 2023-01-09 cs.LG

Debangshu Banerjee , Aditya Gopalan This is my paper · ORCID
classification cs.LG
keywords actionregretspaceswidebanditslinearminimaxvariety
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As noted in the works of \cite{lattimore2020bandit}, it has been mentioned that it is an open problem to characterize the minimax regret of linear bandits in a wide variety of action spaces. In this article we present an optimal regret lower bound for a wide class of convex action spaces.

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  1. Tangential Randomization in Linear Bandits (TRAiL): Guaranteed Inference and Regret Bounds

    stat.ML 2024-11 conditional novelty 7.0 of 10

    TRAiL, a tangential forced-exploration algorithm for linear bandits, achieves Omega(sqrt(T)) inference quality and O(sqrt(T) log T) regret with high probability, and a new lower bound shows regret and inference qualit...

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