stat.ML · 2026-07-26 · No. 65

Machine Learning, 2026-07-26.

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

01 — The papers

2 entries
  1. 01

    Transformer-based Diffusion models for Hydrological Time Series Probabilistic Imputation and Forecasting

    Ferdinand Bhavsar, Lionel Benoit, Maxime Savatier, Edith Gabriel

    stat.ML · cs.LG

    The modeling of hydrometeorological time series with limited observations is a key challenge in the monitoring of hydro-systems and water resources, as well as for flood or drought risk assessment. Due to the high variability of the underlying processes and the sparsity of available measurements, traditional statistical approaches often struggle to accurately represent their dynamics. In this context, recent advances in deep learning offer a...

    arxiv.org/abs/2607.21200 · PDF

  2. 02

    Automatic knot selection in smooth additive models

    Nicolás Carrizosa, Vanesa Guerrero, María Durbán

    stat.ML · cs.LG

    B-spline regression constitutes a widely used framework for nonparametric modeling. The performance of this methodology depends on specifying the number and placement of changepoints, known as knots, prior to the estimation process. Such knot sequence determines the dimension of the B-spline basis used to represent the regression function and the number of coefficients to be estimated. Therefore, the knots' choice affects the model's...

    arxiv.org/abs/2607.21083 · PDF

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