523 citations · 643 across the 14 of their papers we have counts for
16 papers
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…
Learning complexity to guide light-induced self-organized nanopatterns
Eduardo Brandao, Anthony Nakhoul, Stefan Duffner +7
Ultrafast laser irradiation can induce spontaneous self-organization of surfaces into dissipative structures with nanoscale reliefs. These surface patterns emerge from symmetry-bre…
Fast Multiscale Diffusion on Graphs
Sibylle Marcotte, Amélie Barbe, Rémi Gribonval +4
Diffusing a graph signal at multiple scales requires computing the action of the exponential of several multiples of the Laplacian matrix. We tighten a bound on the approximation e…
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…
Metric Learning from Imbalanced Data
Léo Gautheron, Emilie Morvant, Amaury Habrard +1
A key element of any machine learning algorithm is the use of a function that measures the dis/similarity between data points. Given a task, such a function can be optimized with a…
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…
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting
Léo Gautheron, Pascal Germain, Amaury Habrard +3
We propose a Gradient Boosting algorithm for learning an ensemble of kernel functions adapted to the task at hand. Unlike state-of-the-art Multiple Kernel Learning techniques that…
L-SVMs: Landmarks-based Linear Local Support Vectors Machines
Valentina Zantedeschi, Rémi Emonet, Marc Sebban
For their ability to capture non-linearities in the data and to scale to large training sets, local Support Vector Machines (SVMs) have received a special attention during the past…
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…
Theoretical Analysis of Domain Adaptation with Optimal Transport
Ievgen Redko, Amaury Habrard, Marc Sebban
Domain adaptation (DA) is an important and emerging field of machine learning that tackles the problem occurring when the distributions of training (source domain) and test (target…
Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms
Valentina Zantedeschi, Rémi Emonet, Marc Sebban
Many theoretical results in the machine learning domain stand only for functions that are Lipschitz continuous. Lipschitz continuity is a strong form of continuity that linearly bo…
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…
Subspace Alignment For Domain Adaptation
Basura Fernando, Amaury Habrard, Marc Sebban +1
In this paper, we introduce a new domain adaptation (DA) algorithm where the source and target domains are represented by subspaces spanned by eigenvectors. Our method seeks a doma…
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…