most citedMultiple change-point detection for Poisson point processes

1 citations

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7 papers

math.PR2026

Large deviations at the edge for 1D gases and tridiagonal random matrices at high temperature

Charlie Dworaczek Guera, Ronan Memin

We consider a model of a gas of confined particles subject to a two-body repulsive interaction, namely the one-dimensional log or Riesz gas. We are interested in the so-called…

math.PR2026

On the Wasserstein distance between a hyperuniform point process and its mean

Raphael Butez, Sandrine Dallaporta, David García-Zelada

We study the existence of bounds on the expected -Wasserstein distance between a random measure and its mean under the assumption that the -th centered moments of the countin…

stat.ML2026

Latent Guided Sampling for Combinatorial Optimization

Sobihan Surendran, Adeline Fermanian, Sylvain Le Corff

Combinatorial Optimization problems are widespread in domains such as logistics, manufacturing, and drug discovery, yet their NP-hard nature makes them computationally challenging.…

stat.ME2026★ 1 cited

Multiple change-point detection for Poisson point processes

C. Dion-Blanc, D. Hawat, E. Lebarbier +1

The aim of change-point detection is to identify behavioral shifts within time series data. This article focuses on scenarios where the data is derived from an inhomogeneous Poisso…

math.PR2026

On the spectral radius of the ratio of Girko matrices

Djalil Chafaï, David García-Zelada, Yuan Yuan Xu

Girko matrices have independent and identically distributed entries of mean zero and unit variance. In this note, we consider the random matrix model formed by the ratio of two ind…

stat.ML2026

Maxitive Donsker-Varadhan Formulation for Possibilistic Variational Inference

Jasraj Singh, Shelvia Wongso, Jeremie Houssineau +1

Variational inference (VI) is a cornerstone of modern Bayesian learning, enabling approximate inference in complex models. However, its formulation depends on expectations and dive…