59 citations · 570 across the 34 of their papers we have counts for
32 papers · 1 filter
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…
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…
Cooperative Online Learning: Keeping your Neighbors Updated
Nicolò Cesa-Bianchi, Tommaso R. Cesari, Claire Monteleoni
We study an asynchronous online learning setting with a network of agents. At each time step, some of the agents are activated, requested to make a prediction, and pay the correspo…