5 papers
UniRank: Unimodal Bandit Algorithm for Online Ranking
Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont
We tackle a new emerging problem, which is finding an optimal monopartite matching in a weighted graph. The semi-bandit version, where a full matching is sampled at each iteration,…
Unimodal Mono-Partite Matching in a Bandit Setting
Romaric Gaudel, Matthieu Rodet
We tackle a new emerging problem, which is finding an optimal monopartite matching in a weighted graph. The semi-bandit version, where a full matching is sampled at each iteration,…
s-LIME: Reconciling Locality and Fidelity in Linear Explanations
Romaric Gaudel, Luis Galárraga, Julien Delaunay +2
The benefit of locality is one of the major premises of LIME, one of the most prominent methods to explain black-box machine learning models. This emphasis relies on the postulate…
Position-Based Multiple-Play Bandits with Thompson Sampling
Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont
Multiple-play bandits aim at displaying relevant items at relevant positions on a web page. We introduce a new bandit-based algorithm, PB-MHB, for online recommender systems which…
Hybrid Recommender System based on Autoencoders
Florian Strub, Romaric Gaudel, Jérémie Mary
A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratin…