arXiv:2511.01154v2 Announce Type: replace-cross
Abstract: In this short note, we characterize stability of the Kim–Milman flow map — also known as the probability flow ODE — with respect to variations in the target measure in relative Fisher inform…
arXiv:2604.03218v1 Announce Type: cross
Abstract: Suppose we observe data from a distribution $P$ and we wish to test the composite null hypothesis that $P\in\mathscr P$ against a composite alternative $P\in \mathscr Q\subseteq \mathscr P^c$. Herbert …
arXiv:2510.14523v2 Announce Type: replace
Abstract: Selecting the latent dimensions (ranks) in tensor factorization is a central challenge that often relies on heuristic methods. This paper introduces a rigorous approach to determine rank identifiabil…
arXiv:2603.20025v2 Announce Type: replace-cross
Abstract: We study adversarial learning when the target distribution factorizes according to a known Bayesian network. For interpolative divergences, including $(f,\Gamma)$-divergences, we prove a new in…
arXiv:2507.18021v2 Announce Type: replace-cross
Abstract: We study the zeroth-order query complexity of sampling from a general logconcave distribution: given access to an evaluation oracle for a convex function $V:\mathbb{R}^{d}\rightarrow\mathbb{R}\…
arXiv:2602.16142v2 Announce Type: replace-cross
Abstract: We study density estimation in Kullback-Leibler divergence: given an i.i.d. sample from an unknown density $p^\star$, the goal is to construct an estimator $\widehat{p}$ such that $\mathrm{KL}(…
arXiv:2604.01267v1 Announce Type: cross
Abstract: Singular statistical models arise whenever different parameter values induce the same distribution, leading to non-identifiability and a breakdown of classical asymptotic theory. While existing approac…
arXiv:2604.02150v1 Announce Type: cross
Abstract: Samplets are data adapted multiresolution analyses of localized discrete signed measures. They can be constructed on scattered data sites in arbitrary dimension and such that they exhibit vanishing mom…
arXiv:2405.15132v5 Announce Type: replace
Abstract: The Intrinsic Dimension (ID) is a key concept in unsupervised learning and feature selection, as it is a lower bound to the number of variables which are necessary to describe a system. However, in a…
arXiv:2410.22729v4 Announce Type: replace
Abstract: Stochastic differential equations (SDEs) are a fundamental tool for modelling dynamic processes, including gene regulatory networks (GRNs), contaminant transport, financial markets, and image generat…