stat.ML · 2026-07-31 · No. 70

Machine Learning, 2026-07-31.

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

    Doubly Robust Functional Representation Learning for Longitudinal Causal Inference with Irregular Histories

    Mengfei Ran, Yifeng Shen, Ruijie Guan

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

    Longitudinal causal studies often record histories as irregular functional fragments: laboratory values, physiologic signals, sensor streams, and image-derived summaries measured at unequal and informative times. Standard doubly robust estimators usually require scalar summaries, whereas sequence learners optimize prediction losses that need not stabilize the efficient influence function. We propose Doubly Robust Functional Representation...

    arxiv.org/abs/2607.28567 · PDF

  2. 02

    Uncertainty quantification for trustworthy deep learning: Methods and measures

    H. Martin Gillis, Thomas Trappenberg

    stat.ML · cs.LG

    The deployment of deep neural networks in safety-critical domains demands reliable estimates of predictive confidence, yet conventional architectures lack principled uncertainty quantification. This survey provides a structured, critical review of methods for Uncertainty Quantification (UQ) in deep learning, scoped to ensemble-based and approximate Bayesian approaches and the measures used to summarize their outputs. Relative to existing UQ...

    arxiv.org/abs/2607.28248 · PDF

  3. 03

    On a joint simultaneous learning of relevant feature subsets and subspaces in regression-like problems

    Illia Horenko

    stat.ML · cs.AI · cs.LG

    We extend a recently introduced Entropy-Optimal Manifold Clustering (EOMC) to allow for a joint simultaneous identification of subsets and subspaces of relevant features in nonstationary and nonlinear regression problems. It is shown that the proposed extension - that we coin as Entropy-Optimal Manifold Regression (EOMR) - allows a robust learning with linearly-scaling iteration and memory complexities. EOMR is compared to the most complete...

    arxiv.org/abs/2607.28080 · PDF

  4. 04

    Generalization and Trade-off in Adversarial Training: An RKHS Perspective via Kernel Integral Operators

    Yiling Xie, Xiaoming Huo

    stat.ML · cs.LG

    Adversarial training has emerged as a powerful approach for protecting models against adversarial attacks in a broad range of real-world applications. In this paper, we study adversarial training in the reproducing kernel Hilbert space (RKHS) framework through the associated kernel integral operator. We first derive source-uniform generalization error bounds for the RKHS adversarial training estimator in terms of the robustness level, sample...

    arxiv.org/abs/2607.27995 · PDF

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