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6 papers

stat.ME2026

A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems

Maxime Bouton, Pierre-Antoine Thouvenin, Audrey Repetti +1

Markov Chain Monte Carlo (MCMC) algorithms are standard approaches to solve imaging inverse problems and quantify estimation uncertainties, a key requirement in absence of ground-t…

math.CV2026

Monte Carlo methods on compact complex manifolds using Bergman kernels

Thibaut Lemoine, Rémi Bardenet

In this paper, we propose a new randomized method for numerical integration on a compact complex manifold with respect to a continuous volume form. Taking for quadrature nodes a su…

cs.LG2026

Quenched large deviations for Monte Carlo integration with Coulomb gases

Martin Rouault, Rémi Bardenet, Mylène Maïda

Gibbs measures, such as Coulomb gases, are popular in modelling systems of interacting particles. Recently, we proposed to use Gibbs measures as randomized numerical integration al…

cs.LG2026

Monte Carlo with kernel-based Gibbs measures: Guarantees for probabilistic herding

Martin Rouault, Rémi Bardenet, Mylène Maïda

Kernel herding belongs to a family of deterministic quadratures that seek to minimize the maximum mean discrepancy (MMD), that is, the worst-case integration error over a reproduci…

cs.AI2026

IDEQ -- Improving Diffusion Models for the Traveling Salesman Problem (TSP) by Leveraging the Structure of the Solution Space

Mickael Basson, Philippe Preux

We investigate diffusion models to solve the Traveling Salesman Problem. Building on the recent DIFUSCO and T2TCO approaches, we propose IDEQ. IDEQ improves the quality of the solu…

cs.GT2026

Generalized binary utility functions and fair allocations

Franklin Camacho, Rigoberto Fonseca-Delgado, Ramón Pino Pérez +1

The problem of finding envy-free allocations of indivisible goods can not always be solved; therefore, it is common to study some relaxations such as envy-free up to one good (EF1)…