59 citations · 570 across the 34 of their papers we have counts for
36 papers
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
Yuko Kuroki, Alberto Rumi, Taira Tsuchiya +2
We study best-of-both-worlds algorithms for -armed linear contextual bandits. Our algorithms deliver near-optimal regret bounds in both the adversarial and stochastic regimes, w…
Sum-max Submodular Bandits
Stephen Pasteris, Alberto Rumi, Fabio Vitale +1
Many online decision-making problems correspond to maximizing a sequence of submodular functions. In this work, we introduce sum-max functions, a subclass of monotone submodular fu…
Nonstochastic Bandits with Composite Anonymous Feedback
Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni +2
We investigate a nonstochastic bandit setting in which the loss of an action is not immediately charged to the player, but rather spread over the subsequent rounds in an adversaria…
Locally-Adaptive Nonparametric Online Learning
Ilja Kuzborskij, Nicolò Cesa-Bianchi
One of the main strengths of online algorithms is their ability to adapt to arbitrary data sequences. This is especially important in nonparametric settings, where performance is m…
Stochastic Bandits with Delay-Dependent Payoffs
Leonardo Cella, Nicolò Cesa-Bianchi
Motivated by recommendation problems in music streaming platforms, we propose a nonstationary stochastic bandit model in which the expected reward of an arm depends on the number o…
Distribution-Dependent Analysis of Gibbs-ERM Principle
Ilja Kuzborskij, Nicolò Cesa-Bianchi, Csaba Szepesvári
Gibbs-ERM learning is a natural idealized model of learning with stochastic optimization algorithms (such as Stochastic Gradient Langevin Dynamics and ---to some extent--- Stochast…