stat.ML · 2026-08-18 · No. 88

Machine Learning, 2026-08-18.

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

01 — The papers

5 entries
  1. 01

    Non-Crossing Deep Quantile Regression for Distributional Survival Prediction

    Shuai Huang, Zhe Qu, Zhaowei Hua, Guohao Shen, Rui Tang, Hongtu Zhu

    stat.ML · cs.LG · stat.AP

    In survival analysis the way covariates act on the risk of an event often differs between early and late failure times, yet hazard- and mean-based summaries collapse this variation into a single number. Quantile-based modeling instead describes the full conditional distribution on the original time scale, but existing censored-data methods are either inflexible or produce logically inconsistent crossing quantile curves. We propose a Censored...

    arxiv.org/abs/2608.16864 · PDF

  2. 02

    Hide&Seek: Learning to Explain in an End-to-End Differentiable Network

    Tal Ellinson, Hadi Mohasel Afshar, Sally Cripps

    stat.ML · cs.LG

    Instance-wise feature selection is a valuable tool for interpreting labeled data and the predictions of black-box models. In contrast to global feature selection techniques, instance-wise methods dynamically identify important features for each instance. A growing number of methods learn a selector, which identifies important features, and a predictor, which uses these to make predictions. However, these pioneering methods face challenges...

    arxiv.org/abs/2608.16689 · PDF

  3. 03

    Density-Reweighted Entropic Optimal Transport: Decoupling Geometry from Sampling Density

    Keyi Li, Yuval Kluger, Boris Landa

    stat.ML · cs.LG · stat.AP · stat.ME

    Dataset alignment is a central step in data analysis across science and engineering, where the goal is to match observations between datasets. Entropic Optimal Transport (EOT) offers a computationally tractable framework for this task by encoding cross-dataset affinities in a transport plan. However, when two datasets are sampled from geometrically similar low-dimensional structures with substantially different sampling densities, the EOT...

    arxiv.org/abs/2608.16506 · PDF

  4. 04

    Improved Regret Analysis for Parallel Gaussian Process Bandit Optimization

    Shion Takeno, Shogo Iwazaki

    stat.ML · cs.LG

    This paper studies the regret analysis for parallel Gaussian process (GP) bandit optimization. The known regret upper bounds for the widely used GP batched upper confidence bound and GP batched Thompson sampling (GP-BTS) suffer from a multiplicative factor with respect to the batch size $Q$. To avoid this degradation, existing analyses require a polynomial number of uncertainty sampling (US) for $Q$ at the beginning of optimization. However,...

    arxiv.org/abs/2608.16492 · PDF

  5. 05

    LiD-GLM: Lipschitz-constrained Deep Generalized Linear Models

    Tom Splittgerber, Niklas Koenen, Marvin N. Wright, Werner Brannath

    stat.ML · cs.LG

    The combination of traditional statistical models and neural network (NN) components into semi-structured hybrid models is an intriguing approach to construct models that, ideally, combine traditional interpretability with the unprecedented flexibility of NNs. In order to preserve interpretability, it is usually necessary to restrict the NN components to prevent them from dominating the model. However, existing methods that enforce structural...

    arxiv.org/abs/2608.16340 · PDF

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