8 papers
Leveraging Similarities in Multi-Armed Bandits
Khaled Eldowa, Thibaud Rahier, Augustin Cablant +2
In many online learning and bandit problems, the actions we consider possess inherent similarities--for instance because they share latent traits, tags, or hierarchical structure.…
Instance-Dependent Regret Bounds for Nonstochastic Linear Partial Monitoring
Federico Di Gennaro, Khaled Eldowa, Nicolò Cesa-Bianchi
In contrast to the classic formulation of partial monitoring, linear partial monitoring can model infinite outcome spaces, while imposing a linear structure on both the losses and…
Online Episodic Convex Reinforcement Learning
Bianca Marin Moreno, Khaled Eldowa, Pierre Gaillard +2
We study online learning in episodic finite-horizon Markov decision processes (MDPs) with convex objective functions, known as the concave utility reinforcement learning (CURL) pro…
Improved Regret Bounds for Bandits with Expert Advice
Nicolò Cesa-Bianchi, Khaled Eldowa, Emmanuel Esposito +1
In this research note, we revisit the bandits with expert advice problem. Under a restricted feedback model, we prove a lower bound of order for the worst-cas…
Information Capacity Regret Bounds for Bandits with Mediator Feedback
Khaled Eldowa, Nicolò Cesa-Bianchi, Alberto Maria Metelli +1
This work addresses the mediator feedback problem, a bandit game where the decision set consists of a number of policies, each associated with a probability distribution over a com…
General Tail Bounds for Non-Smooth Stochastic Mirror Descent
Khaled Eldowa, Andrea Paudice
In this paper, we provide novel tail bounds on the optimization error of Stochastic Mirror Descent for convex and Lipschitz objectives. Our analysis extends the existing tail bound…
On the Minimax Regret for Online Learning with Feedback Graphs
Khaled Eldowa, Emmanuel Esposito, Tommaso Cesari +1
In this work, we improve on the upper and lower bounds for the regret of online learning with strongly observable undirected feedback graphs. The best known upper bound for this pr…
Information-Theoretic Regret Bounds for Bandits with Fixed Expert Advice
Khaled Eldowa, Nicolò Cesa-Bianchi, Alberto Maria Metelli +1
We investigate the problem of bandits with expert advice when the experts are fixed and known distributions over the actions. Improving on previous analyses, we show that the regre…