17 citations · 79 across the 16 of their papers we have counts for
3 papers · 1 filter
Group Meritocratic Fairness in Linear Contextual Bandits
Riccardo Grazzi, Arya Akhavan, John Isak Texas Falk +2
We study the linear contextual bandit problem where an agent has to select one candidate from a pool and each candidate belongs to a sensitive group. In this setting, candidates' r…
Meta Representation Learning with Contextual Linear Bandits
Leonardo Cella, Karim Lounici, Massimiliano Pontil
Meta-learning seeks to build algorithms that rapidly learn how to solve new learning problems based on previous experience. In this paper we investigate meta-learning in the settin…
Multi-task Representation Learning with Stochastic Linear Bandits
Leonardo Cella, Karim Lounici, Grégoire Pacreau +1
We study the problem of transfer-learning in the setting of stochastic linear bandit tasks. We consider that a low dimensional linear representation is shared across the tasks, and…