Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Gaudel, Romaric"'
Autor:
Biswas, Sayan, Frey, Davide, Gaudel, Romaric, Kermarrec, Anne-Marie, Lerévérend, Dimitri, Pires, Rafael, Sharma, Rishi, Taïani, François
This paper introduces ZIP-DL, a novel privacy-aware decentralized learning (DL) algorithm that exploits correlated noise to provide strong privacy protection against a local adversary while yielding efficient convergence guarantees for a low communic
Externí odkaz:
http://arxiv.org/abs/2403.11795
Autor:
Kelodjou, Gwladys, Rozé, Laurence, Masson, Véronique, Galárraga, Luis, Gaudel, Romaric, Tchuente, Maurice, Termier, Alexandre
Publikováno v:
AAAI Conference on Artificial Intelligence, 2024
Machine learning techniques, such as deep learning and ensemble methods, are widely used in various domains due to their ability to handle complex real-world tasks. However, their black-box nature has raised multiple concerns about the fairness, trus
Externí odkaz:
http://arxiv.org/abs/2312.12115
Publikováno v:
Complex Feedback in Online Learning Workshop at the 39th International Conference on Machine Learning, Jul 2022, Baltimore, United States
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, has been addressed by \cite{ADMA}, creating an algorithm with an expe
Externí odkaz:
http://arxiv.org/abs/2208.01515
Autor:
Gaudel, Romaric, Rodet, Matthieu
Publikováno v:
Complex Feedback in Online Learning Workshop at the 39th International Conference on Machine Learning, Jul 2022, Baltimore, United States
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, has been addressed by \cite{ADMA}, creating an algorithm with an expe
Externí odkaz:
http://arxiv.org/abs/2208.01511
Publikováno v:
Symposium on Intelligent Data Analysis (IDA'22), Apr 2022, Rennes, France
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 that the more locally we look at the vicinity of an instance, the simp
Externí odkaz:
http://arxiv.org/abs/2208.01510
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 uses the Thompson sampling framework. This algorithm handles a display
Externí odkaz:
http://arxiv.org/abs/2009.13181
Publikováno v:
the 1st Workshop on Deep Learning for Recommender Systems, Sep 2016, Boston, United States. pp.11 - 16, 2016
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 ratings. In the last decades, few attempts where done to handle that object
Externí odkaz:
http://arxiv.org/abs/1606.07659
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 ratings. They can be enhanced by adding side information to tackle the wel
Externí odkaz:
http://arxiv.org/abs/1603.00806
Autor:
Gaudel, Romaric
Nous nous intéressons à la sélection de modèle en apprentissage automatique, sous deux angles différents. La première partie de la thèse concerne les méthodes à noyau relationnel. Les méthodes à noyau permettent en principe de s'affranchir
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 rankings exist in the literature, here we focus on the Area Under the
Externí odkaz:
http://arxiv.org/abs/1508.06091