stat.ML · 2026-09-16 · No. 115

Machine Learning, 2026-09-16.

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

    Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Bias

    Nicolas Alexander Ihlo, Merle Behr

    stat.ML · cs.LG

    In various fields, such as medicine and marketing, accurately predicting individual treatment effects holds significant promise. However, achieving reliable predictions alone is often insufficient for making informed decisions; it is equally important to understand why the treatment effect is higher for some individuals than for others. To address this two-fold challenge of prediction and interpretation, we introduce an algorithm based on...

    arxiv.org/abs/2609.16971 · PDF

  2. 02

    On the disintegration of the stochastic majority vote: From PAC-Bayesian bounds to a self-bounding algorithm

    Julien Bastian, Benjamin Leblanc, Pascal Germain, Amaury Habrard, Guillaume Metzler, Emilie Morvant, Paul Viallard

    stat.ML · cs.LG

    Weighted majority votes are central to many successful ensemble methods. PAC-Bayesian theory provides tight generalization guarantees for such models by analyzing the expected risk of stochastic classifiers, while analyzing the risk of deterministic majority votes relies on surrogate bounds. To avoid these surrogates, Zantedeschi et al. ( 2021) introduced guarantees for stochastic majority votes, but the resulting models remain randomized. In...

    arxiv.org/abs/2609.16803 · PDF

  3. 03

    Time-warping estimation via stationarity-based learning of the de-warped signal

    Corentin Presvôts, Adrien Meynard

    stat.ML · cs.LG

    Time-warping estimation is a fundamental problem in signal processing with applications in bioacoustics, radar, and biomedical analysis. This paper introduces a Time-Warping Estimation Trainable (TWET) model for estimating timewarping functions from a single observation. The proposed approach formulates time-warping estimation as a stationarization problem in the wavelet domain and leverages a hierarchical dilated convolutional architecture...

    arxiv.org/abs/2609.16796 · PDF

  4. 04

    Certified Inference and Training for Deep Equilibrium Networks: A Continuation Framework with Polynomial Complexity Guarantees

    Alex Borisevich

    stat.ML · cs.LG

    We develop a certified continuation framework for equilibrium computation and for training deep equilibrium networks (DEQs), with training formulated as interpolation to accuracy $2^{-b}$. For inference, compact input homotopy selects a unique branch from a supplied start root, and a rounded Newton tracker follows it under certified boundary, conditioning, derivative, and tube-radius bounds. For training, we augment local-plus-low-rank...

    arxiv.org/abs/2609.16485 · PDF

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