arXiv:2509.12356v1 Announce Type: cross Abstract: We prove ratio-consistency of the jackknife variance estimator, and certain variants, for a broad class of generalized U-statistics whose variance is asymptotically dominated by their H’ajek projection, with the classical fixed-order case recovered as a…
arXiv:2309.15828v3 Announce Type: replace Abstract: Recent literature has explored various ways to improve soft sensors by utilizing learning algorithms with transferability. A performance gain is generally attained when knowledge is transferred among strongly related soft sensor learning tasks. One setting…
arXiv:2509.12884v1 Announce Type: cross Abstract: Nonstationary spatial processes can often be represented as stationary processes on a warped spatial domain. Selecting an appropriate spatial warping function for a given application is often difficult and, as a result of this, warping…
arXiv:2410.20659v2 Announce Type: replace Abstract: Despite significant research on the optimization aspects of federated learning, the exploration of generalization error, especially in the realm of heterogeneous federated learning, remains an area that has been insufficiently investigated, primarily limited to developments…
arXiv:2509.12889v1 Announce Type: cross Abstract: This paper addresses the statistical estimation of Gaussian Mixture Models (GMMs) with unknown diagonal covariances from independent and identically distributed samples. We employ the Beurling-LASSO (BLASSO), a convex optimization framework that promotes sparsity in the…
arXiv:2509.04852v2 Announce Type: replace Abstract: Estimating density ratios is a fundamental problem in machine learning, but existing methods often trade off accuracy for efficiency. We propose textit{Interval-annealed Secant Alignment Density Ratio Estimation (ISA-DRE)}, a framework that enables accurate, any-step estimation…
arXiv:2509.12917v1 Announce Type: cross Abstract: Deep Equilibrium Models (DEQs) are an interesting class of implicit model where the model output is implicitly defined as the fixed point of a learned function. These models have been shown to outperform explicit (fixed-depth)…
arXiv:2509.12981v1 Announce Type: cross Abstract: Quantile Partial Effect (QPE) is a statistic associated with conditional quantile regression, measuring the effect of covariates at different levels. Our theory demonstrates that when the QPE of cause on effect is assumed to lie…
arXiv:2209.01754v4 Announce Type: replace-cross Abstract: The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as those that will be faced when the decision rule is deployed. However, in a number…
arXiv:2509.13130v1 Announce Type: cross Abstract: We make three contributions to conformal prediction. First, we propose fuzzy conformal confidence sets that offer a degree of exclusion, generalizing beyond the binary inclusion/exclusion offered by classical confidence sets. We connect fuzzy confidence sets…
