168 citations · 202 across the 9 of their papers we have counts for
10 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…
Hybrid Collaborative Filtering with Autoencoders
Florian Strub, Jeremie Mary, Romaric Gaudel
Collaborative Filtering aims at exploiting the feedback of users to provide personalised recommendations. Such algorithms look for latent variables in a large sparse matrix of rati…
AUC Optimisation and Collaborative Filtering
Charanpal Dhanjal, Romaric Gaudel, Stephan Clemencon
In recommendation systems, one is interested in the ranking of the predicted items as opposed to other losses such as the mean squared error. Although a variety of ways to evaluate…
Bandits Warm-up Cold Recommender Systems
Jérémie Mary, Romaric Gaudel, Preux Philippe
We address the cold start problem in recommendation systems assuming no contextual information is available neither about users, nor items. We consider the case in which we only ha…
Online Matrix Completion Through Nuclear Norm Regularisation
Charanpal Dhanjal, Romaric Gaudel, Stéphan Clémençon
It is the main goal of this paper to propose a novel method to perform matrix completion on-line. Motivated by a wide variety of applications, ranging from the design of recommende…
Efficient Eigen-updating for Spectral Graph Clustering
Charanpal Dhanjal, Romaric Gaudel, Stéphan Clémençon
Partitioning a graph into groups of vertices such that those within each group are more densely connected than vertices assigned to different groups, known as graph clustering, is…