arXiv:2510.15058v3 Announce Type: replace-cross
Abstract: Kernel Stein discrepancies (KSDs) have emerged as a powerful tool for quantifying goodness-of-fit over the last decade, featuring numerous successful applications. To the best of our knowledge,…
arXiv:2603.27414v1 Announce Type: cross
Abstract: Statistical estimation often involves tradeoffs between expensive, high-quality measurements and a variety of lower-quality proxies. We introduce Multiple-Prediction-Powered Inference (MultiPPI): a gen…
arXiv:2206.05829v2 Announce Type: replace-cross
Abstract: We study the problem of learning multivariate dependencies in nonparametric and high-dimensional settings. This includes but is not limited to graphical models. Our approach effectively combine…
arXiv:2603.24763v2 Announce Type: replace-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 i…
arXiv:2603.26048v1 Announce Type: new
Abstract: We study rank selection for low-rank tensor regression under random covariates design. Under a Gaussian random-design model and some mild conditions, we derive population expressions for the expected tra…
arXiv:2603.25796v1 Announce Type: new
Abstract: We provide explicit, finite-sample guarantees for learning causal representations from data with a sublinear number of environments. Causal representation learning seeks to provide a rigourous foundation…
arXiv:2603.25934v1 Announce Type: cross
Abstract: In this work, we investigate how to develop sharp concentration inequalities for sub-Weibull random variables, including sub-Gaussian and sub-exponential distributions. Although the random variables ma…
arXiv:2502.14950v2 Announce Type: replace-cross
Abstract: Inferring causal models from observed correlations is a challenging task, crucial to many areas of science. In order to alleviate the computational effort when sifting through possible causal e…
arXiv:2603.26301v1 Announce Type: cross
Abstract: Many causal discovery algorithms, including the celebrated FCI algorithm, output a Partial Ancestral Graph (PAG). PAGs serve as an abstract graphical representation of the underlying causal structure, …
arXiv:2603.25466v1 Announce Type: new
Abstract: We study statistical estimation in a student–teacher setting, where predictions from a pre-trained teacher are used to guide a student model. A standard approach is to train the student to directly matc…