stat.ML · 2026-05-25 · No. 9

Machine Learning, 2026-05-25.

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

01 — The papers

2 entries
  1. 01

    KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis

    Stelios Boulitsakis Logothetis, Angela Wood, Pietro Li ò

    stat.ML · cs.AI · cs.LG

    Survival analysis aims to model how covariates and time jointly shape the time-to-event distribution under right censoring. Classical methods such as the Cox model and generalised additive models (GAMs) require interactions and time-varying effects to be manually specified, which is increasingly impractical on rich clinical datasets. We introduce KAPLAN-HR, a B-spline Kolmogorov-Arnold Network (KAN) for nonparametric estimation of the...

    arxiv.org/abs/2605.23082 · PDF

  2. 02

    Finite-Particle Convergence Rates for Conservative and Non-Conservative Drifting Models

    Krishnakumar Balasubramanian

    stat.ML · cs.AI · cs.LG · math.ST

    We propose and analyze a conservative drifting method for one-step generative modeling. The method replaces the original displacement-based drifting velocity by a kernel density estimator (KDE)-gradient velocity, namely the difference of the kernel-smoothed data score and the kernel-smoothed model score. This velocity is a gradient field, addressing the non-conservatism issue identified for general displacement-based drifting fields. We prove...

    arxiv.org/abs/2605.22795 · PDF

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