526 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR83 papers
- Université Jean MonnetFR62 papers
- Lyon 1 UniversitéFR39 papers
- Institut d’Optique Graduate SchoolFR13 papers
- Leading Health CareSE13 papers
- Laboratoire Traitement et Communication de l’InformationFR12 papers
- Télécom ParisFR11 papers
- Centre de Recherche Astrophysique de LyonFR10 papers
- Université de LyonFR10 papers
- École Normale Supérieure de LyonFR9 papers
- Institut Polytechnique de ParisFR9 papers
- Université Paris-SaclayFR9 papers
20 papers · 1 filter
Méthode de quadrature pour les PINNs fondée théoriquement sur la hessienne des résiduels
Antoine Caradot, Rémi Emonet, Amaury Habrard +2
Physics-informed Neural Networks (PINNs) have emerged as an efficient way to learn surrogate neural solvers of PDEs by embedding the physical model in the loss function and minimiz…
Pattern-Based Graph Classification: Comparison of Quality Measures and Importance of Preprocessing
Lucas Potin, Rosa Figueiredo, Vincent Labatut +1
Graph classification aims to categorize graphs based on their structural and attribute features, with applications in diverse fields such as social network analysis and bioinformat…
Fair Text Classification via Transferable Representations
Thibaud Leteno, Michael Perrot, Charlotte Laclau +2
Group fairness is a central research topic in text classification, where reaching fair treatment between sensitive groups (e.g., women and men) remains an open challenge. We propos…
Federated learning compression designed for lightweight communications
Lucas Grativol Ribeiro, Mathieu Leonardon, Guillaume Muller +2
Federated Learning (FL) is a promising distributed method for edge-level machine learning, particularly for privacysensitive applications such as those in military and medical doma…
Relax and penalize: a new bilevel approach to mixed-binary hyperparameter optimization
Sara Venturini, Marianna de Santis, Jordan Patracone +3
In recent years, bilevel approaches have become very popular to efficiently estimate high-dimensional hyperparameters of machine learning models. However, to date, binary parameter…
Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurement
Lucas Potin, Rosa Figueiredo, Vincent Labatut +1
In the context of public procurement, several indicators called red flags are used to estimate fraud risk. They are computed according to certain contract attributes and are theref…
A PAC-Bayes Analysis of Adversarial Robustness
Paul Viallard, Guillaume Vidot, Amaury Habrard +1
We propose the first general PAC-Bayesian generalization bounds for adversarial robustness, that estimate, at test time, how much a model will be invariant to imperceptible perturb…
All of the Fairness for Edge Prediction with Optimal Transport
Charlotte Laclau, Ievgen Redko, Manvi Choudhary +1
Machine learning and data mining algorithms have been increasingly used recently to support decision-making systems in many areas of high societal importance such as healthcare, ed…
Deep Neural Networks Are Congestion Games: From Loss Landscape to Wardrop Equilibrium and Beyond
Nina Vesseron, Ievgen Redko, Charlotte Laclau
The theoretical analysis of deep neural networks (DNN) is arguably among the most challenging research directions in machine learning (ML) right now, as it requires from scientists…
Near-Optimal Comparison Based Clustering
Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar
The goal of clustering is to group similar objects into meaningful partitions. This process is well understood when an explicit similarity measure between the objects is given. How…
An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data
Rémi Viola, Rémi Emonet, Amaury Habrard +3
In this paper, we address the challenging problem of learning from imbalanced data using a Nearest-Neighbor (NN) algorithm. In this setting, the minority examples typically belong…
Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs
Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi
We consider the fully decentralized machine learning scenario where many users with personal datasets collaborate to learn models through local peer-to-peer exchanges, without a ce…
Feature Selection for Unsupervised Domain Adaptation using Optimal Transport
Léo Gautheron, Ievgen Redko, Carole Lartizien
In this paper, we propose a new feature selection method for unsupervised domain adaptation based on the emerging optimal transportation theory. We build upon a recent theoretical…
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning
François Denis, Mattias Gybels, Amaury Habrard
Learning probabilistic models over strings is an important issue for many applications. Spectral methods propose elegant solutions to the problem of inferring weighted automata fro…
Supervised Metric Learning with Generalization Guarantees
Aurélien Bellet
The crucial importance of metrics in machine learning algorithms has led to an increasing interest in optimizing distance and similarity functions, an area of research known as met…
A Survey on Metric Learning for Feature Vectors and Structured Data
Aurélien Bellet, Amaury Habrard, Marc Sebban
The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good…
Robustness and Generalization for Metric Learning
Aurélien Bellet, Amaury Habrard
Metric learning has attracted a lot of interest over the last decade, but the generalization ability of such methods has not been thoroughly studied. In this paper, we introduce an…
Similarity Learning for Provably Accurate Sparse Linear Classification
Aurelien Bellet, Amaury Habrard, Marc Sebban
In recent years, the crucial importance of metrics in machine learning algorithms has led to an increasing interest for optimizing distance and similarity functions. Most of the st…
Learning rules from multisource data for cardiac monitoring
Marie-Odile Cordier, Elisa Fromont, René Quiniou
This paper formalises the concept of learning symbolic rules from multisource data in a cardiac monitoring context. Our sources, electrocardiograms and arterial blood pressure meas…
Extraction de concepts sous contraintes dans des données d'expression de gènes
Baptiste Jeudy, François Rioult
In this paper, we propose a technique to extract constrained formal concepts.