Efficient Matroid Bandit Linear Optimization Leveraging Unimodality

TL;DR

This paper introduces a method for matroid bandit linear optimization that leverages unimodality to significantly reduce time complexity and oracle calls without sacrificing regret performance.

Contribution

It exploits the unimodal structure of the problem to limit membership oracle calls to logarithmic double-logarithmic iterations, improving efficiency.

Findings

No regret loss compared to state-of-the-art methods

Reduced time complexity in experiments

Fewer calls to membership oracle

Abstract

We study the combinatorial semi-bandit problem under matroid constraints. The regret achieved by recent approaches is optimal, in the sense that it matches the lower bound. Yet, time complexity remains an issue for large matroids or for matroids with costly membership oracles (e.g. online recommendation that ensures diversity). This paper sheds a new light on the matroid semi-bandit problem by exploiting its underlying unimodal structure. We demonstrate that, with negligible loss in regret, the number of iterations involving the membership oracle can be limited to \mathcal{O}(\log \log T)$. This results in an overall improved time complexity of the learning process. Experiments conducted on various matroid benchmarks show (i) no loss in regret compared to state-of-the-art approaches; and (ii) reduced time complexity and number of calls to the membership oracle.