stat.ML · 2026-08-25 · No. 95

Machine Learning, 2026-08-25.

4 new papers in stat.ML. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

4 entries
  1. 01

    Primal--Dual Alternating Neural Learning for Timely Classification with Performance Guarantees

    Jiaming Qiu, Yingye Zheng, Ying-Qi Zhao

    stat.ML · cs.LG · stat.ME

    Timely risk classification is essential in many clinical monitoring settings, where decisions must balance the benefit of classifying patients early for subsequent intervention against the value of observing additional data. Yet most existing statistical and machine-learning methods are designed for fully observed trajectories and offer limited control over key operating characteristics such as sensitivity, specificity, and monitoring cost....

    arxiv.org/abs/2608.23480 · PDF

  2. 02

    One Inverse Step is a Convex Program: Bayes-Limit Calibration of Diffusion Inversion

    Gordei Verbii

    stat.ML · cs.LG

    One implicit DDIM inversion step is the cheapest probe of whether a pretrained diffusion model encodes local manifold geometry. It is the stationarity condition of an explicit potential, $x-G(x)=\nablaΨ_t(x)$, strongly convex at the Bayes limit with modulus exactly $e^{-h_t}$ for the step's log-SNR gap $h_t$ $-$ for every data law, schedule and point, with no manifold, reach or unimodality hypothesis. Three consequences must be kept apart....

    arxiv.org/abs/2608.23094 · PDF

  3. 03

    A Commutator Framework for Selective Spectral Alignment in Deep Neural Networks

    Kaj Nyström

    stat.ML · cs.LG

    We develop a finite-width geometric framework describing how learned feature geometries are organized, transported, and selectively aligned in deep neural networks. Incompatibility among weight-generated covariance, gates, and backward sensitivities is quantified through three families of commutators: between gates and covariance, between sensitivities and covariance, and between average gradient outer products (AGOPs) and neural feature...

    arxiv.org/abs/2608.22910 · PDF

  4. 04

    Generative Neural Networks for Sinkhorn Distributionally Robust Hypothesis Testing

    Fenglin Zhang, Teyan Liu, Jie Wang

    stat.ML · cs.LG · math.OC

    This paper studies the Sinkhorn distributionally robust hypothesis testing (SDRHT) problem, seeking a robust detector against least-favorable distributions in Sinkhorn discrepancy-based ambiguity sets centered at the empirical distributions. Existing approaches solve this problem by solving large-scale conic programs, which are not scalable. To overcome this, we propose a generative framework that learns least-favorable distributions and...

    arxiv.org/abs/2608.22746 · PDF

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