16 citations · 40 across the 14 of their papers we have counts for
6 papers · 1 filter
Multitask Online Learning: Listen to the Neighborhood Buzz
Juliette Achddou, Nicolò Cesa-Bianchi, Pierre Laforgue
We study multitask online learning in a setting where agents can only exchange information with their neighbors on an arbitrary communication network. We introduce $\texttt{MT-CO}_…
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning
Pier Giuseppe Sessa, Pierre Laforgue, Nicolò Cesa-Bianchi +1
Multitask learning is a powerful framework that enables one to simultaneously learn multiple related tasks by sharing information between them. Quantifying uncertainty in the estim…
Linear Bandits with Memory: from Rotting to Rising
Giulia Clerici, Pierre Laforgue, Nicolò Cesa-Bianchi
Nonstationary phenomena, such as satiation effects in recommendations, have mostly been modeled using bandits with finitely many arms. However, the richer action space provided by…
AdaTask: Adaptive Multitask Online Learning
Pierre Laforgue, Andrea Della Vecchia, Nicolò Cesa-Bianchi +1
We introduce and analyze AdaTask, a multitask online learning algorithm that adapts to the unknown structure of the tasks. When the tasks are stochastically activated, we show…
A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits
Pierre Laforgue, Giulia Clerici, Nicolò Cesa-Bianchi +1
Motivated by the fact that humans like some level of unpredictability or novelty, and might therefore get quickly bored when interacting with a stationary policy, we introduce a no…
Multitask Online Mirror Descent
Nicolò Cesa-Bianchi, Pierre Laforgue, Andrea Paudice +1
We introduce and analyze MT-OMD, a multitask generalization of Online Mirror Descent (OMD) which operates by sharing updates between tasks. We prove that the regret of MT-OMD is of…