3 papers
cs.LG2026
A Perturbation Approach to Unconstrained Linear Bandits
Andrew Jacobsen, Dorian Baudry, Shinji Ito +1
We revisit the standard perturbation-based approach of Abernethy et al. (2008) in the context of unconstrained Bandit Linear Optimization (uBLO). We show the surprising result that…
stat.ML2025
Non-stationary Bandit Convex Optimization: A Comprehensive Study
Xiaoqi Liu, Dorian Baudry, Julian Zimmert +2
Bandit Convex Optimization is a fundamental class of sequential decision-making problems, where the learner selects actions from a continuous domain and observes a loss (but not it…
cs.LG2025
Best-of-Both Worlds for linear contextual bandits with paid observations
Nathan Boyer, Dorian Baudry, Patrick Rebeschini
We study the problem of linear contextual bandits with paid observations, where at each round the learner selects an action in order to minimize its loss in a given context, and ca…