stat.ML · 2026-07-13 · No. 52

Machine Learning, 2026-07-13.

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

    Deep Gaussian Processes on Directed Acyclic Graphs

    Federico L. Perlino, Oliver Hamelijnck, Adam M. Johansen, Theodoros Damoulas

    stat.ML · cs.LG · math.ST · stat.CO · stat.ME

    Many real-world processes can be represented as compositions of functions along a directed acyclic graph (DAG). In causal modelling, these correspond to the underlying mechanisms; in engineering, to multiple fidelity levels; and in gene-regulatory networks, to transcription factors. These functions are partially observed across the DAG, with noisy and heterogeneously sampled measurements, posing significant challenges for reconstruction,...

    arxiv.org/abs/2607.09645 · PDF

  2. 02

    Spectrally Deconfounded Gradient Boosting

    Andrea Nava, Peter Bühlmann, Fabio Sigrist

    stat.ML · cs.LG

    Flexible machine-learning methods can be sensitive to hidden confounding: they may learn associations induced by unobserved confounders rather than stable signals. Spectral deconfounding mitigates this problem by shrinking high-variance directions of the covariate matrix that, under dense confounding, carry latent confounder information. Existing work has largely focused on linear models. We develop a nonlinear spectral deconfounding...

    arxiv.org/abs/2607.09371 · PDF

  3. 03

    Influence Diagnostics in High-dimensional M-estimation: Precise Asymptotics

    Hugo Cui

    stat.ML · cs.LG

    The impact of a given training point on a statistical model is classically measured through its leave-one-out influence, which quantifies the effect of its removal from the training set on the model accuracy. While the statistics of leave-one-out influences are well understood in the low-dimensional, large sample limit $n\to \infty, d=O(1)$, they become more intricate in high dimensions, as the influence of a given sample develops non-trivial...

    arxiv.org/abs/2607.09250 · PDF

  4. 04

    Score Accuracy Along the Forward Diffusion Does Not Certify Numerical Stability in Diffusion Sampling

    Yiwei Zhou

    stat.ML · cs.LG · math.NA · math.PR

    Score matching controls average error under the forward marginals, but a discretized reverse-time sampler evaluates the learned score along its own trajectory. We show that small forward-marginal error does not guarantee numerical stability. We construct a single smooth score field with arbitrarily small forward-marginal $L^2$ error. The learned reverse-time process is nonexplosive, has moments of every order, and can be arbitrarily close to...

    arxiv.org/abs/2607.08757 · PDF

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