output
20042026
most citedAdaptive Importance Sampling in General Mixture Classes

208 citations

206 papers

q-fin.RM2026

Cloud failure and cyber insurance: calibration of stress scenarios and diversification

Olivier Lopez, Daniel Nkameni

The expansion of the cyber insurance market remains exposed to the threat of accumulation events that could simultaneously affect a large number of policyholders. Although few such…

cs.LG2026

A Wasserstein GAN-based climate scenario generator for risk management and insurance: the case of soil subsidence

Antoine Heranval, Olivier Lopez, Didier Ngatcha +1

According to the United Nations Office for Disaster Risk Reduction (2025), the average annual cost of natural catastrophes increased from 70--80 billion USD between 1970 and 2000 t…

math.OC2026

Importance Sampling Optimization with Laplace Principle

Radu-Alexandru Dragomir, François Portier, Victor Priser

Grid search and random search are widely used techniques for hyperparameter tuning in machine learning, especially when gradient information is unavailable. In these methods, a fin…

math.ST2026

Concentration of the bootstrap empirical process, with applications to statistical inference

Guillaume Maillard, Adrien Saumard

Considering a general framework of bootstrap with exchangeable weights, we show some concentration inequalities for the supremum of the bootstrap empirical process. On the one hand…

math.ST2026

A Kernel Two-Sample Test Invariant under Group Action with Applications to Functional Data

Madison Giacofci, Anouar Meynaoui, Alex Podgorny

We introduce a kernel-based two-sample test for comparing probability distributions up to group actions. Our construction yields invariant kernels for locally compact -compact g…

stat.ME2026

An information criterion for detecting periodicities in functional time series

Rinka Sagawa, Yan Liu, Valentin Patilea

We propose an information criterion for determining an unknown number of periodic components in functional time series. Identifying the number of frequencies in large-scale time se…