6 papers
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…
Parameter-free Dynamic Regret: Time-varying Movement Costs, Delayed Feedback, and Memory
Hao Qiu, Andrew Jacobsen, Emmanuel Esposito +1
In this paper, we study dynamic regret in unconstrained online convex optimization (OCO) with movement costs. Specifically, we generalize the standard setting by allowing the movem…
Gradient-Variation Regret Bounds for Unconstrained Online Learning
Yuheng Zhao, Andrew Jacobsen, Nicolò Cesa-Bianchi +1
We develop parameter-free algorithms for unconstrained online learning with regret guarantees that scale with the gradient variation $V_T(u) = \sum_{t=2}^T \|\nabla f_t(u)-\nabla f…
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…
Dynamic Regret Reduces to Kernelized Static Regret
Andrew Jacobsen, Alessandro Rudi, Francesco Orabona +1
We study dynamic regret in online convex optimization, where the objective is to achieve low cumulative loss relative to an arbitrary benchmark sequence. By observing that competin…
An Equivalence Between Static and Dynamic Regret Minimization
Andrew Jacobsen, Francesco Orabona
We study the problem of dynamic regret minimization in online convex optimization, in which the objective is to minimize the difference between the cumulative loss of an algorithm…