3 papers
cs.LG2026
Parameter-Free Dynamic Regret for Unconstrained Linear Bandits
Alberto Rumi, Andrew Jacobsen, Nicolò Cesa-Bianchi +1
We study dynamic regret minimization in unconstrained adversarial linear bandit problems. In this setting, a learner must minimize the cumulative loss relative to an arbitrary sequ…
cs.LG2025
Stochastic Shortest Path with Sparse Adversarial Costs
Emmeran Johnson, Alberto Rumi, Ciara Pike-Burke +1
We study the adversarial Stochastic Shortest Path (SSP) problem with sparse costs under full-information feedback. In the known transition setting, existing bounds based on Online…
cs.LG2024
Bandits with Abstention under Expert Advice
Stephen Pasteris, Alberto Rumi, Maximilian Thiessen +4
We study the classic problem of prediction with expert advice under bandit feedback. Our model assumes that one action, corresponding to the learner's abstention from play, has no…