arXiv:2504.09279v2 Announce Type: replace
Abstract: We introduce a novel generative modeling framework based on a discretized parabolic Monge-Amp\`{e}re PDE, which emerges as a continuous limit of the Sinkhorn algorithm commonly used in optimal transp…
arXiv:2604.00942v1 Announce Type: new
Abstract: We introduce a differentially private manifold denoising framework that allows users to exploit sensitive reference datasets to correct noisy, non-private query points without compromising privacy. The m…
arXiv:2604.00672v1 Announce Type: new
Abstract: TF-IDF is a classical formula that is widely used for identifying important terms within documents. We show that TF-IDF-like scores arise naturally from the test statistic of a penalized likelihood-ratio…
arXiv:2505.21580v3 Announce Type: replace
Abstract: Complex data are often represented as a graph, which in turn can often be viewed as a realisation of a random graph, such as an inhomogeneous random graph model (IRG). For general fast goodness-of-fi…
arXiv:2505.21770v3 Announce Type: replace-cross
Abstract: Identifying the drift and diffusion of an SDE from its population dynamics is a notoriously challenging task. Researchers in machine learning and single-cell biology have only been able to prov…
arXiv:2604.00064v1 Announce Type: new
Abstract: We study trajectory forecasting under squared loss for time series with weak conditional structure, using highly expressive prediction models. Building on the classical characterization of squared-loss r…
arXiv:2604.00848v1 Announce Type: cross
Abstract: High-dimensional statistical settings ($p \gg n$) pose fundamental challenges for classical inference, largely due to bias introduced by regularized estimators such as the LASSO. To address this, Javan…
arXiv:2602.17525v2 Announce Type: replace
Abstract: In variational inference (VI), the practitioner approximates a high-dimensional distribution $\pi$ with a simple surrogate one, often a (product) Gaussian distribution. However, in many cases of prac…
arXiv:2507.11780v2 Announce Type: replace-cross
Abstract: Constructing confidence intervals for the value of an (unknown) optimal treatment policy is a fundamental problem in causal inference. Insight into the optimal policy value can guide the develo…
arXiv:2511.11161v2 Announce Type: replace-cross
Abstract: This paper addresses the nonparametric estimation of the drift function over a compact domain for a time-homogeneous diffusion process, based on high-frequency discrete observations from $N$ in…