arXiv:2603.24763v1 Announce Type: cross
Abstract: Conditional independence is central to modern statistics, but beyond special parametric families it rarely admits an exact covariance characterization. We introduce the binary expansion group intersect…
arXiv:2504.21419v3 Announce Type: replace
Abstract: We introduce kernel density machines (KDM), an agnostic kernel-based framework for learning the Radon-Nikodym derivative (density) between probability measures under minimal assumptions. KDM applies …
arXiv:2601.07325v2 Announce Type: replace
Abstract: We introduce the $\widetilde{\rho}$-posterior, a modified version of the $\rho$-posterior, obtained by replacing the supremum over competitor parameters with a softmax aggregation. This modification …
arXiv:2602.12023v3 Announce Type: replace-cross
Abstract: Applied work under interference typically models outcomes as functions of own treatment and a low-dimensional exposure mapping of others’ treatments, even when that mapping may be misspecified….
arXiv:2603.25622v1 Announce Type: cross
Abstract: We present an efficient algorithm for uniformly sampling from an arbitrary compact body $\mathcal{X} \subset \mathbb{R}^n$ from a warm start under isoperimetry and a natural volume growth condition. Ou…
arXiv:2602.20844v2 Announce Type: replace-cross
Abstract: Stochastic network models play a central role across a wide range of scientific disciplines, and questions of statistical inference arise naturally in this context. In this paper we investigate…
arXiv:2603.05335v2 Announce Type: replace-cross
Abstract: Four distinct admissibility geometries govern sequential and distribution-free inference: Blackwell risk dominance over convex risk sets, anytime-valid admissibility within the nonnegative supe…
arXiv:2402.08151v4 Announce Type: replace-cross
Abstract: Importance sampling (IS) is an efficient stand-in for model refitting in performing (LOO) cross-validation (CV) on a Bayesian model. IS inverts the Bayesian update for a single observation by r…
arXiv:2511.09500v4 Announce Type: replace-cross
Abstract: We study the problem of denoising when only the noise level is known, not the noise distribution. Independent noise $Z$ corrupts a signal $X$, yielding the observation $Y = X + \sigma Z$ with k…
arXiv:2603.24493v1 Announce Type: new
Abstract: Uniform laws of large numbers form a cornerstone of Vapnik–Chervonenkis theory, where they are characterized by the finiteness of the VC dimension. In this work, we study uniform convergence phenomena i…