stat.ML · 2026-06-29 · No. 38

Machine Learning, 2026-06-29.

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

    Surprises in Proper Positive-Only Learning

    Shai Ben-David, Farnam Mansouri, Anay Mehrotra, Manolis Zampetakis

    stat.ML · cs.LG · math.ST

    Binary classification from positive-only samples is a variant of PAC learning in which the learner receives i.i.d. samples from the positive region of an unknown target concept, but is evaluated under the original distribution (which places mass on both positive and negative regions). This model dates back to Natarajan [1987, STOC], and the characterization of improper learning is well-known -- it even appears in textbooks. The...

    arxiv.org/abs/2606.28309 · PDF

  2. 02

    Adversarial Contamination Meets Hard Thresholding: An Iterative Algorithm with Signal Adaptivity and Minimax Optimality

    Shixiang Liu, Hanming Yang

    stat.ML · cs.LG

    Pervasive data contamination -- stemming from measurement errors, outliers, or adversarial corruption -- has motivated the development of robust statistical methods. In this context, we propose a two-stage Adversarial Contamination-resistant Iterative Hard Thresholding (AC-IHT) algorithm for high-dimensional regression with contamination. Our nonconvex algorithm achieves minimax near-optimal (up to logarithmic terms) estimation by iteratively...

    arxiv.org/abs/2606.27685 · PDF

  3. 03

    The Decision Geometry of Covariance Estimation for the Global Minimum-Variance Portfolio under Heavy Tails

    Xavier Fonseca

    stat.ML · cs.LG · q-fin.PM

    The global minimum-variance portfolio (GMVP) is the canonical decision built from an estimated covariance matrix, yet covariance estimators are universally evaluated by matrix-norm loss, which is not the object the decision depends on. We characterise exactly how covariance-estimation error maps into GMVP suboptimality. We prove an exact regret identity and a non-asymptotic bound showing decision regret depends on the estimation error only...

    arxiv.org/abs/2606.27462 · PDF

  4. 04

    Directed Graph Topology Inference via Graph Filter Identification

    Rasoul Shafipour, Andrei Buciulea, Santiago Segarra, Antonio G. Marques, Gonzalo Mateos

    stat.ML · cs.LG · cs.SI · eess.SP

    We address the problem of inferring a directed network from nodal measurements generated by linear diffusion dynamics on the sought graph. Observations are modeled as the outputs of a graph convolutional filter, i.e., a polynomial (with unknown coefficients) of a local diffusion graph-shift operator encoding the latent graph topology, excited with an ensemble of independent graph signals with arbitrarily-correlated nodal components. Unlike...

    arxiv.org/abs/2606.27455 · PDF

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