4 citations · 8 across the 7 of their papers we have counts for
4 papers · 1 filter
Embedding Learning on Multiplex Networks for Link Prediction
Orell Trautmann, Olaf Wolkenhauer, Clémence Réda
Over the past years, embedding learning on networks has shown tremendous results in link prediction tasks for complex systems, with a wide range of real-life applications. Learning…
Handling Missing Data in Downstream Tasks With Distribution-Preserving Guarantees
Rahul Bordoloi, Clémence Réda, Saptarshi Bej +1
Missing feature values are a significant hurdle for downstream machine-learning tasks such as classification. However, imputation methods for classification might be time-consuming…
Near-Optimal Collaborative Learning in Bandits
Clémence Réda, Sattar Vakili, Emilie Kaufmann
This paper introduces a general multi-agent bandit model in which each agent is facing a finite set of arms and may communicate with other agents through a central controller in or…
Top-m identification for linear bandits
Clémence Réda, Emilie Kaufmann, Andrée Delahaye-Duriez
Motivated by an application to drug repurposing, we propose the first algorithms to tackle the identification of the m 1 arms with largest means in a linear bandit model, in…