stat.ML · 2026-07-02 · No. 41

Machine Learning, 2026-07-02.

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

01 — The papers

5 entries
  1. 01

    Characterizing and Identifying Separable Graphical Models

    Christopher Meek, Kayvan Sadeghi

    stat.ML · cs.LG · math.ST

    We study a broad class of graphical models whose independencies correspond to vertex separation in mixed graphs with directed, undirected, and bidirected edges, that are capable of encoding independence structures arising from feedback, latent and selection mechanisms. In particular, we introduce separable graphs, in which each missing edge implies the existence of a separating set for its endpoints, and essentially separable graphs, those...

    arxiv.org/abs/2607.01057 · PDF

  2. 02

    Function-Counting Theory for Low-Dimensional Data Structures

    Konstantin Häberle, Helmut Bölcskei

    stat.ML · cs.IT · cs.LG · math.CA · math.CO

    The success of deep learning models in classification and regression is widely attributed to the low-dimensional structure that real-world data tend to exhibit, despite their high-dimensional representation. This work attempts to provide a mathematical framework for binary classification on low-dimensional data, building on Cover's (1965) function-counting theory. With our framework, we aim to address the question of how the low-dimensional...

    arxiv.org/abs/2607.01010 · PDF

  3. 03

    Deep Multitask Learning for Mixed-Type Outcomes with Shared Sparsity

    Huichao Li, Tong Wang, Sanguo Zhang, Shuangge Ma

    stat.ML · cs.LG

    Most existing multitask learning approaches are limited by their reliance on task-specific loss functions tailored to the scale and type of each outcome. When outcomes differ across tasks, these losses are generally not directly comparable, which makes it difficult to formulate a unified objective and may limit information sharing across tasks. We propose a multitask transformation framework in which task-specific responses may differ through...

    arxiv.org/abs/2607.00995 · PDF

  4. 04

    Neural Network-Based Estimation of Time-Dependent Parameters in AR(p) Processes

    Agnieszka Kopeć, Paweł Przybyłowicz, Martyna Wiącek

    stat.ML · cs.LG

    We investigate a forecasting framework based on a simple discrete-time dynamic model with coefficients varying in time. The parameters of the model are recovered within a deep learning framework, which makes it possible to retain a transparent parametric structure while simultaneously accounting for complex and nonstationary patterns in the observed phenomenon. Our analysis covers two specifications of the noise process. Besides the standard...

    arxiv.org/abs/2607.00470 · PDF

  5. 05

    From Spectral Methods to Sample Complexity Bounds for Fourier Neural Operators

    Nisha Chandramoorthy, Daniel Sanz-Alonso, Nathan Waniorek

    stat.ML · cs.LG · math.NA

    We establish approximation and learning guarantees for Fourier neural operators (FNOs) applied to time-$T$ solution operators of dissipative evolution equations. The analysis builds on the premise that FNOs can efficiently approximate and learn solution operators whenever these operators admit stable and accurate spectral discretizations. To formalize this idea, we introduce classes of evolution operators defined through spectral methods and...

    arxiv.org/abs/2607.00320 · PDF

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