stat.ML · 2026-08-07 · No. 77

Machine Learning, 2026-08-07.

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

01 — The papers

7 entries
  1. 01

    Scalable estimation of VARMA models

    Daniel Paulin, Victor Elvira

    stat.ML · cs.LG · math.ST

    Vector autoregressive moving-average (VARMA) models have long been considered impractical beyond moderate dimensions: the likelihood is non-convex, the parametrization is identified only up to equivalence, and every evaluation costs a pass over the entire series. Yet their moving-average term captures with a few parameters what a pure autoregression matches only with many lags. We introduce an estimation framework that removes this...

    arxiv.org/abs/2608.06340 · PDF

  2. 02

    Optimal Rates for Learning with Monotone Adversaries

    Anay Mehrotra

    stat.ML · cs.DS · cs.LG · math.ST

    A monotone adversary observes an i.i.d. labeled sample and appends a finite number of further examples of its choice, every one of them labeled correctly by the target hypothesis. The learner sees a uniform shuffle of the combined sample and is scored on the original distribution. Every example is correctly labeled, but the insertions depend on the clean sample, so the combined sample is not exchangeable. Larsen, Pabbaraju, and Shetty, who...

    arxiv.org/abs/2608.06337 · PDF

  3. 03

    Stochastic Dynamics on Persistence Diagram Space via Reinforcement Learning

    Farzana Nasrin

    stat.ML · cs.LG · math.AT

    Persistence diagrams (PDs) provide stable and interpretable summaries of multiscale topological structure. While substantial progress has been made in the statistical analysis of PDs, existing literature often treats diagrams as static objects and provide limited frameworks for probabilistic modeling and stochastic evolution on PD space. We introduce a reinforcement learning framework for stochastic dynamics on PD space, where diagrams evolve...

    arxiv.org/abs/2608.06276 · PDF

  4. 04

    Minimax Optimal Early-Stopped Gradient Descent for Gaussian Mixture Classification

    Alex Buna, Shirley Xiaoqi Liu, Patrick Rebeschini

    stat.ML · cs.LG

    In overparameterised classification, training data can be linearly separable even when the underlying distribution is not. In this setting, gradient descent (GD) on the logistic loss diverges in norm while converging in direction to a max-margin interpolating classifier, whose implicit bias can be statistically suboptimal. In this work, we show that early stopping can overcome this suboptimality: in a Gaussian mixture model with...

    arxiv.org/abs/2608.06250 · PDF

  5. 05

    Beyond Marginal Validity: Finite-Sample Guarantees for Localized Conformal Prediction

    Anton Conrad, Rustam Isaev, Denis Belomestny, Eric Moulines, Sergey Samsonov

    stat.ML · cs.LG

    Conformal prediction endows arbitrary black-box predictors with finite-sample, distribution-free marginal coverage, yet marginal validity can hide severe covariate-specific miscalibration, while exact distribution-free conditional coverage is finite-sample unattainable. Randomly localized conformal prediction (RLCP) mitigates this gap by calibrating near the test point while preserving marginal coverage. Existing theory, however, lacks...

    arxiv.org/abs/2608.06206 · PDF

  6. 06

    Handling Missing Data in Probabilistic Regression Trees

    Taiane Schaedler Prass, Alisson Silva Neimaier, Guilherme Pumi

    stat.ML · cs.LG

    Probabilistic Regression Trees (PRTrees) are a smooth and consistent alternative to classical regression trees, producing continuous predictions through probabilistic split assignments. This paper extends the PRTree framework to accommodate missing predictor values directly during tree construction, eliminating the need for prior imputation. Three strategies are proposed, each exploiting the available information differently: a...

    arxiv.org/abs/2608.06195 · PDF

  7. 07

    Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data

    Nina van Gerwen, Dimitris Rizopoulos, Manon Hillegers, Loes Keijsers, Sten Willemsen

    stat.ML · cs.LG

    The experience sampling method (ESM) is a longitudinal research design where participants report their thoughts, emotional states and behaviours multiple times a day. Our work is motivated by such data collected by the GrowIt! app, which was released to investigate daily emotions among adolescents during the COVID-19 pandemic. Current procedures to analyse ESM data face various challenges. While standard statistical techniques may not scale...

    arxiv.org/abs/2608.05930 · PDF

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