AI RESEARCH
A Perturbation Approach to Unconstrained Linear Bandits
arXiv CS.LG
•
ArXi:2603.28201v1 Announce Type: new We revisit the standard perturbation-based approach of Abernethy in the context of unconstrained Bandit Linear Optimization (uBLO). We show the surprising result that in the unconstrained setting, this approach effectively reduces Bandit Linear Optimization (BLO) to a standard Online Linear Optimization (OLO) problem. Our framework improves on prior work in several ways.