stat.ML · 2026-08-12 · No. 82

Machine Learning, 2026-08-12.

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

    Conditional Independence Tests for Constraint-Based Causal Discovery: A Survey

    Pavel Averin, Theodoros Moysiadis, Ioannis Katakis

    stat.ML · cs.LG

    Conditional Independence (CI) tests are the statistical engine of constraint-based causal discovery: in algorithms such as PC (Peter-Clark) and FCI (Fast Causal Inference), skeleton pruning and key orientations follow directly from CI decisions. This survey reviews CI testing with emphasis on assumptions, robustness, and scalability in high-dimensional and mixed-type settings common in biomedical domains. The survey organizes widely used CI...

    arxiv.org/abs/2608.11156 · PDF

  2. 02

    Self-Normalized Inference for Constant-Stepsize Temporal-Difference Learning under Markovian Sampling

    Min Zeng, Yichen Zhang, Xiaofeng Shao

    stat.ML · cs.LG

    Constant-stepsize temporal-difference (TD) learning is attractive for policy evaluation, but inference from a single Markov trajectory must account for serial dependence and a stepsize-dependent stationary target. For fixed-stepsize linear TD, we establish a functional central limit theorem whose covariance retains the multiplicative component induced by the random TD matrix and the stationary iterate error. We then derive a joint functional...

    arxiv.org/abs/2608.10896 · PDF

  3. 03

    Spectral Embeddings of Degree-$α$ Laplacians in Random Dot Product Graphs

    John Park, Ning Hao

    stat.ML · cs.LG

    Spectral clustering methods for network data are commonly based on a few matrix representations, such as the adjacency matrix and the symmetric Laplacian. We study a continuum of degree-normalized spectral embeddings that includes these commonly used choices as special cases. Under a random dot product graph model, we establish a row-wise central limit theorem for this family of embeddings. The result provides an explicit description of how...

    arxiv.org/abs/2608.10845 · PDF

  4. 04

    Iterative Erasure Count Is Not an Affine-Invariant Concept Dimension

    Tingan Jin, Shuhang Dong, Haosong Li, Chung-Hsien Chou

    stat.ML · cs.CV · cs.LG

    How many directions does a neural representation use to encode a concept? A common answer repeatedly erases probe directions and reports the stopping count or cumulative removed rank. We show that both quantities can change under an information-preserving invertible reparameterization, so neither is intrinsically a concept dimension. We distinguish model-defined population quantities (generating dimension, sufficient linear dimension, and...

    arxiv.org/abs/2608.10566 · 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.