stat.ML · 2026-07-09 · No. 48

Machine Learning, 2026-07-09.

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

01 — The papers

6 entries
  1. 01

    A Unified Detection Framework for AI-Related Content and Artifacts

    Xifeng Zhang, Tao Hu, Yijie Peng, Wan Tian

    stat.ML · cs.LG

    Artificial intelligence (AI) is a double-edged sword: while it has achieved remarkable success across a wide range of domains, its deployment also calls for effective oversight and regulation, for which the detection of AI-related content and artifacts is perhaps the most direct and cost-effective approach. To this end, we propose a unified detection framework based on Mahalanobis distance scores (MDS), applicable to several important...

    arxiv.org/abs/2607.07527 · PDF

  2. 02

    Statistical inverse learning and $\ell^1$-regularization

    Abhishake Rastogi, Tatiana A. Bubba, Tapio Helin, Luca Ratti

    stat.ML · cs.LG · math.ST

    We study the recovery of sparse functions from finite, noisy, and indirect observations in the framework of statistical inverse learning. The unknown is modeled as an element of $\ell^1$, and observations are generated through a possibly nonlinear forward operator $A:\ell^1\to H$, where $H$ is a vector-valued reproducing kernel Hilbert space. We propose an $\ell^1$-regularized empirical risk minimizer and develop a theoretical analysis of its...

    arxiv.org/abs/2607.07468 · PDF

  3. 03

    DiPhon: Diffusion on Graphons for Scalable Graph Generation

    Sergio Rozada, Yiming Qin, Manuel Madeira, Pascal Frossard, Alejandro Ribeiro

    stat.ML · cs.AI · cs.LG

    Diffusion models represent a leading paradigm for graph generation, with notable impact in domains such as molecular design. Yet, scaling these models to large graphs remains an open problem. We approach this question in the dense-graph setting through the lens of graphons, the size-agnostic limit objects of dense graph sequences, to study how structural graph statistics behave across node-size scales. This perspective leads to DiPhon, a...

    arxiv.org/abs/2607.07232 · PDF

  4. 04

    Tensorized algorithms and scalable filtering methods for hidden Markov and factorial hidden Markov models

    Roxana Barrios, Ioannis Sgouralis

    stat.ML · cs.LG

    A common method for the representation and analysis of time-series data is the hidden Markov model (HMM), where each observation is associated with a hidden state that evolves over time. However, many real-world systems are influenced by multiple independent factors, which are more naturally represented by factorial hidden Markov models (fHMM), where several hidden Markov chains jointly generate the observed data. Although an fHMM provides a...

    arxiv.org/abs/2607.07008 · PDF

  5. 05

    Finding a stationary point of a stochastic convex problem

    Felipe Areces, John Duchi, Malo Sommers

    stat.ML · cs.LG · math.OC

    We consider the problem of finding stationary points for stochastic convex optimization problems. Rather than surrogates to stationarity, such as a proximity-to-stationarity guarantee or small gradient of the Moreau envelope, we ask for a stronger notion: that the subdifferential of the objective actually contains a small element. This criterion is non-trivial, because subdifferentials of convex functions fail to converge uniformly, even in...

    arxiv.org/abs/2607.06883 · PDF

  6. 06

    Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling

    Robert Gruhlke, Julius Berner, David Sommer, Lorenz Richter

    stat.ML · cs.LG

    Diffusion models offer a powerful framework for sampling from complex probability densities by learning to reverse a noising process. A common approach involves solving for the time-reversed stochastic differential equation (SDE), which requires the score function of the evolving sample distribution. The logarithm of this distribution's density is governed by a Hamilton-Jacobi-Bellman (HJB) type partial differential equation (PDE). However,...

    arxiv.org/abs/2607.06841 · PDF

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