We study online linear regression problems in a distributed setting, where the data is spread
over a network. In each round, each network node proposes a linear predictor, with the …

This monograph provides an overview of distributed online optimization in multi-agent
systems. Online optimization approaches planning and decision problems from a robust …

We consider the problem of online linear regression in the stochastic setting. We derive high
probability regret bounds for online $\textit {ridge} $ regression and the $\textit {forward} …

We consider learning a stochastic bandit model, where the reward function belongs to a
general class of uniformly bounded functions, and the additive noise can be …

We consider the setting of online linear regression for arbitrary deterministic sequences, with
the square loss. We are interested in the aim set by Bartlett et al.(2015): obtain regret …

This paper addresses the distributed online bandit linear regression problems with privacy
protection, in which the training data are spread in a multi-agent network. Each node …

We study the problem of online generalized linear regression in the stochastic setting, where
the label is generated from a generalized linear model with possibly unbounded additive …

This thesis explores the challenge of making reinforcement learning (RL) more suitable to
real-world problems without loosing theoretical guarantees. This is an interesting active …

Ce travail présente quelques contributions théoriques et pratiques à la prévision des suites
arbitraires. Dans ce domaine, la prévision se déroule séquentiellement en même temps que …