activity
20132026
most citedLittelmann path model for geometric crystals, Whittaker functions on Lie groups and Brownian motion

18 citations · 22 across the 8 of their papers we have counts for

collaborators
Showing 2024Show all

6 papers · 1 filter

math.PR2024

Matsumoto-Yor processes on Jordan algebras

Reda Chhaibi, Manon Defosseux

The process , where is a real Brownian motion, is known as the geometric 2M-X Matsumoto--Yor process. Remarkably, it enjoys the Markov…

stat.ML2024

Sensitivity Analysis for Active Sampling, with Applications to the Simulation of Analog Circuits

Reda Chhaibi, Fabrice Gamboa, Christophe Oger +3

We propose an active sampling flow, with the use-case of simulating the impact of combined variations on analog circuits. In such a context, given the large number of parameters, i…

nlin.SI2024

Scattering of the Toda system and the Gaussian -ensemble

Reda Chhaibi

The classical Toda flow is a well-known integrable Hamiltonian system that diagonalizes matrices. By keeping track of the distribution of entries and precise scattering asymptotics…

cs.CV2024

Statistical Edge Detection And UDF Learning For Shape Representation

Virgile Foy, Fabrice Gamboa, Reda Chhaibi

In the field of computer vision, the numerical encoding of 3D surfaces is crucial. It is classical to represent surfaces with their Signed Distance Functions (SDFs) or Unsigned Dis…

stat.ML2024

Training More Robust Classification Model via Discriminative Loss and Gaussian Noise Injection

Hai-Vy Nguyen, Fabrice Gamboa, Sixin Zhang +3

Robustness of deep neural networks to input noise remains a critical challenge, as naive noise injection often degrades accuracy on clean (uncorrupted) data. We propose a novel tra…

stat.ML2024

Combining Statistical Depth and Fermat Distance for Uncertainty Quantification

Hai-Vy Nguyen, Fabrice Gamboa, Reda Chhaibi +3

We measure the Out-of-domain uncertainty in the prediction of Neural Networks using a statistical notion called ``Lens Depth'' (LD) combined with Fermat Distance, which is able to…