stat.ML · 2026-09-17 · No. 116

Machine Learning, 2026-09-17.

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

    A General Kernel Framework for Non-CND Distance Measures Using |D|-Dimensional Sparse Landmark Embeddings

    Marcus M. Noack, Maher B. Alghalayini, Mark D. Risser

    stat.ML · cs.LG · math.PR

    Kernel methods, and Gaussian Processes (GPs) in particular, require a Hilbertian distance measure---one whose square is conditionally negative definite (CND)---to guarantee positive semi-definiteness (PSD) of the kernel matrix; a condition that fails for many natural input spaces, including smooth manifolds and spaces of probability distributions. We propose the Sparse Landmark Embedding (SLE) kernel, which eliminates this requirement...

    arxiv.org/abs/2609.19083 · PDF

  2. 02

    Fast Learning Rates for Physics-Informed Kernel Methods

    Luc Brogat-Motte, Joachim Bona-Pellissier, Giacomo Meanti, Lorenzo Rosasco

    stat.ML · cs.LG

    In physics-informed machine learning, a target function $u^*$ is learned from noisy value observations $y_i=u^*(x_i)+ \varepsilon_i$, together with differential information, given either by noisy observations $d_j=(Du^*)(z_j)+ξ_j$ or by a known physical constraint $Du^*=v$. We consider the setting where $D$ is a linear differential operator and analyze a physics-informed kernel estimator $\hat u$ combining $n$ value observations and $m$...

    arxiv.org/abs/2609.18901 · PDF

  3. 03

    Rank and computation of the pathlifting Jacobian of a DAG ReLU network

    Manon Verbockhaven

    stat.ML · cs.LG

    This paper provides a self-contained proof of the rank of the pathlifting Jacobian of a DAG ReLU network by performing an induction on the network's number of hidden nodes. In fact, the induction is elementary, and the key recipe is to consider the skeleton matrix of the network, a sparse matrix encoding the network paths, and transform the representation of one of its hidden neurons into an output node. The proof relies on intermediate...

    arxiv.org/abs/2609.18682 · PDF

  4. 04

    Preservation of Log-Concavity and Convergence of Wasserstein-Fisher-Rao Gradient Flows

    Francesca Romana Crucinio, Sahani Pathiraja

    stat.ML · cs.LG · math.PR

    We study the convergence of Wasserstein-Fisher-Rao (WFR) gradient flows for sampling from probability distributions known up to a normalisation constant. By combining Wasserstein transport with Fisher-Rao birth-death dynamics, WFR flows balance exploration and selection. These flows have been recognised as a promising mechanism to accelerate convergence beyond Langevin dynamics. We show that for a class of strongly log-concave target...

    arxiv.org/abs/2609.18118 · PDF

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