stat.ML · 2026-09-21 · No. 120

Machine Learning, 2026-09-21.

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

    Riemannian Simultaneous Inference for Tangent Vector Field Regression

    Xiaotian Chang, Yangdi Jiang, Qirui Hu

    stat.ML · cs.LG · stat.ME

    We consider nonparametric tangent vector field regression on a Riemannian manifold without boundary. Because responses at different points lie in different tangent spaces, the proposed kernel estimator first parallel transports nearby responses to the target tangent space and then forms a volume-corrected local average. We first derive its uniform second-order bias, finite-bandwidth covariance, and stochastic rate. For simultaneous inference,...

    arxiv.org/abs/2609.21910 · PDF

  2. 02

    Improving the Predictive Performance of Bootstrap Aggregating by Dirichlet Resampling

    Quoc Viet Le, Joonha Park

    stat.ML · cs.LG · stat.ME

    We revisit Breiman's observation that reducing inter-tree correlation without weakening individual trees can improve random forests. Building on this principle, we introduce two variants: Dirichlet-Multinomial Bagging Random Forest (DM) and Dirichlet-Weighted Random Forest (DW). Both modulate sample reweighting via a concentration parameter $α>0$. We provide a simple theoretical criterion that clarifies when these variants behave...

    arxiv.org/abs/2609.21454 · PDF

  3. 03

    Brownian Heads for Deep ReLU Representations: Activation Mass and the Cost of Same-Sample Selection

    Mahdi Mohammadigohari, Nicole Mücke

    stat.ML · cs.LG

    Deep representation learning often selects hidden features and fits the final predictor on the same sample, so fixed-feature analysis performed after selection can omit selection cost. We study the conditional empirical Rademacher complexity of deep ReLU representations followed by bounded-norm predictors in additive or Lévy-Brownian RKHSs, termed Brownian heads. For a fixed representation, we derive an exact dual identity and sharp bounds in...

    arxiv.org/abs/2609.21422 · PDF

  4. 04

    Sparse Identification for Automatic Large-Scale Screening: A Constraint-Aware Framework with Ultra Fast Decoding Algorithm

    Jianing Li, Li Chai, Yingcheng Lai

    stat.ML · cs.LG · eess.SP

    In the early stages of a pandemic, identification of a small number of infected individuals through large-scale screening is critical for pandemic control, yet remains challenging under limited reagents and testing capacity. Existing group testing methods suffer from either high computational complexity or low identification accuracy. Even worse, no available methods provide theoretically rigorous analysis for sparse identification with hard...

    arxiv.org/abs/2609.21321 · PDF

  5. 05

    Diagonalized Attention for Individualized Regression: Latent-Row Localization and Prediction

    Borui Peng, Liwei Lin, Feifei Wang, Long Feng

    stat.ML · cs.LG

    Modern text and image representations are often matrix-valued, with rows corresponding to tokens, patches, or other local feature vectors. Predictive information is often sparse but sample-specific, making classical sparse regression methods with a common support poorly suited to this heterogeneity. This paper formalizes an individualized sparse regression framework for matrix-valued covariates in which each observation has its own rows of...

    arxiv.org/abs/2609.21320 · PDF

This edition is part of The Daily Abstract — stat.ML archive. Subscribe to receive these in your inbox each morning, automatically translated to Spanish, with reply-to-PDF: arxivdaily.ignorelist.com.

Colophon Set in Georgia, with system sans for interface chrome and a monospaced stack for code and paper identifiers. Sole accent: amber #D99C5E. Built and served on an always-free VM. The masthead is set 14% letterspaced because newspapers do that and it works.