arXiv:2603.24392v1 Announce Type: cross
Abstract: Privacy and algorithmic fairness have become two central issues in modern machine learning. Although each has separately emerged as a rapidly growing research area, their joint effect remains comparati…
arXiv:2603.23835v1 Announce Type: cross
Abstract: There remain theoretical gaps in deep neural network estimators for the nonparametric Cox proportional hazards model. In particular, it is unclear how gradient-based optimization error propagates to po…
arXiv:2603.24384v1 Announce Type: new
Abstract: The theory of Local Intrinsic Dimensionality (LID) has become a valuable tool for characterizing local complexity within and across data manifolds, supporting a range of data mining and machine learning …
arXiv:2511.20888v2 Announce Type: replace-cross
Abstract: This paper argues that DNNs implement a computational Occam’s razor — finding the `simplest’ algorithm that fits the data — and that this could explain their incredible and wide-ranging succe…
arXiv:2603.24400v1 Announce Type: cross
Abstract: We propose a neural network model for contextual regression in which the regression model depends on contextual features that determine the active submodel and an algorithm to fit the model. The propos…
arXiv:2512.09295v3 Announce Type: replace-cross
Abstract: Consider the additive Gaussian model $Y = X + \sigma Z$, where $X \sim P$ is an unknown signal, $Z \sim N(0,1)$ is independent of $X$, and $\sigma > 0$ is known. Let $Q$ denote the law of $Y$. …