stat.ML · 2026-09-01 · No. 102

Machine Learning, 2026-09-01.

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

01 — The papers

3 entries
  1. 01

    Implementing neural network mixed-effects models in Template Model Builder (TMB)

    Nan Zheng, Hoi Yiu Cheung, Vibhu Sharma, James T. Thorson, Noel G. Cadigan

    stat.ML · cs.LG

    Neural network mixed-effects models (NMMs) have gained traction by combining the strong representation and predictive power of artificial neural networks with the capacity of mixed-effects modeling to capture complex correlation structures. However, existing estimation approaches rely heavily on manual derivations of objective functions and gradients, which inherently forces simplifying approximations and severely constrains the complexity...

    arxiv.org/abs/2608.31133 · PDF

  2. 02

    Learning the Geometry of Admissible Hypotheses through Inductive Bias in Training Distributions

    James Crowley, Faez Ahmed, Anton van Beek

    stat.ML · cs.LG

    Scientific discovery often requires reasoning over competing hypotheses that are consistent with experimental observations. For mixed-variable and combinatorial hypothesis spaces, however, constructing probabilistic representations remains challenging because both the active model components and their associated parameters are unknown. In this work, we present a framework for learning continuous latent representations of admissible partial...

    arxiv.org/abs/2608.31028 · PDF

  3. 03

    Informative Label Missingness in Multiclass Classification Information Geometry and Excess Risk

    Fariborz Setoudehtazang, Geoffrey J. McLachlan

    stat.ML · cs.LG

    Informative label missingness can change the usual efficiency ordering between completely and partially labelled classifiers because the pattern of missing labels may itself carry information about the classification model. We develop a general likelihood-based theory for this phenomenon in parametric multiclass classification. An efficient-information decomposition separates information lost through unavailable class memberships from...

    arxiv.org/abs/2608.30561 · PDF

This edition is part of The Daily Abstract — stat.ML archive. Subscribe to receive these in your inbox each morning, automatically translated to Spanish, with reply-to-PDF: arxivdaily.ignorelist.com.

Colophon Set in Georgia, with system sans for interface chrome and a monospaced stack for code and paper identifiers. Sole accent: amber #D99C5E. Built and served on an always-free VM. The masthead is set 14% letterspaced because newspapers do that and it works.