stat.ML · 2026-08-27 · No. 97

Machine Learning, 2026-08-27.

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

    Efficient Estimation of High Information Projections using Nearest Neighbours

    David P. Hofmeyr

    stat.ML · cs.LG

    An intuitive method for dimensionality reduction is proposed, which is highly effective for finding interesting projections of multivariate data. Following similar intuitive motivation to a number of existing techniques, the proposed method is based on enhancing the nearest neighbour relationships in the data. The proposed projection arises from the spectral decomposition of a matrix designed to encode the local covariance structure in the...

    arxiv.org/abs/2608.25887 · PDF

  2. 02

    Multi-output Gaussian process prediction of physical fields under linear equality constraints

    Mahamat Hamdan Nassouradine, Clément Gauchy, Pierre-Emmanuel Angeli, Sébastien da Veiga

    stat.ML · cs.LG

    We address the simultaneous prediction of multiple high-dimensional physical fields governed by linear equality constraints, a setting that arises in many real-world applications in physics machine learning. Gaussian process (GP) regression is a widely used surrogate modeling approach due to its effectiveness in small-sample regimes and its ability to provide uncertainty quantification. However, applying GP models in this setting raises two...

    arxiv.org/abs/2608.25709 · PDF

  3. 03

    Fast rates in Bayesian online learning with approximate posteriors

    Ilsang Ohn

    stat.ML · cs.LG

    Exact Bayes prediction enjoys fast predictive regret guarantees, but exact posterior updating or representation may be too costly for online use. We study when these statistical guarantees are preserved by computational approximations. We show that the cumulative price of posterior approximation can be governed by the interaction between the contraction radius of the exact Gibbs posterior and the Wasserstein distance between the approximate...

    arxiv.org/abs/2608.25706 · PDF

  4. 04

    Adaptive Regularization for Random Features: A Neighboring Early-Stopping Rule with Oracle-Rate Guarantees

    Caixing Wang, Zhibo Chen, Yue Wang

    stat.ML · cs.LG

    Random feature methods provide a scalable approximation to kernel ridge regression (KRR), but the regularization parameter that yields the oracle learning rate depends on unknown smoothness and capacity parameters. In this work, we propose a neighboring early-stopping rule for adaptive regularization in KRR with random features (KRR-RF). The method uses a grid that is uniform in inverse regularization and compares only adjacent estimators,...

    arxiv.org/abs/2608.25513 · PDF

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