523 citations · 643 across the 14 of their papers we have counts for
8 papers · 1 filter
A Bregman Proximal Viewpoint on Neural Operators
Abdel-Rahim Mezidi, Jordan Patracone, Saverio Salzo +4
We present several advances on neural operators by viewing the action of operator layers as the minimizers of Bregman regularized optimization problems over Banach function spaces.…
Provably Accurate Adaptive Sampling for Collocation Points in Physics-informed Neural Networks
Antoine Caradot, Rémi Emonet, Amaury Habrard +2
Despite considerable scientific advances in numerical simulation, efficiently solving PDEs remains a complex and often expensive problem. Physics-informed Neural Networks (PINN) ha…
A survey on domain adaptation theory: learning bounds and theoretical guarantees
Ievgen Redko, Emilie Morvant, Amaury Habrard +2
All famous machine learning algorithms that comprise both supervised and semi-supervised learning work well only under a common assumption: the training and test data follow the sa…
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…
Similarity Learning for Time Series Classification
Maria-Irina Nicolae, Éric Gaussier, Amaury Habrard +1
Multivariate time series naturally exist in many fields, like energy, bioinformatics, signal processing, and finance. Most of these applications need to be able to compare these st…
Algorithmic Robustness for Learning via -Good Similarity Functions
Maria-Irina Nicolae, Marc Sebban, Amaury Habrard +2
The notion of metric plays a key role in machine learning problems such as classification, clustering or ranking. However, it is worth noting that there is a severe lack of theoret…
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…
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…