stat.ML · 2026-10-01 · No. 130

Machine Learning, 2026-10-01.

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

    Proximal Balancing for Causal Effect Estimation under Unmeasured Confounding

    Yonghan Jung

    stat.ML · cs.LG · stat.ME

    Estimating causal effects from observational data is central to science and policy, but the effects are not identified when confounders are unmeasured. Proximal causal inference addresses this problem with proxies of the unmeasured confounders. However, existing proxy-based approaches either designate proxy roles and solve an inverse problem, which is ill-posed and hard to estimate with high-dimensional proxies, or use a latent-variable...

    arxiv.org/abs/2609.40051 · PDF

  2. 02

    Amortized Bayesian Inference on Multilevel Models of Arbitrary Structure

    Daniel Habermann, Andreas Bulling, Stefan T. Radev, Paul-Christian Bürkner

    stat.ML · cs.LG · stat.CO

    We develop a general method for amortized Bayesian inference on multilevel models of arbitrary structure. Given a generative model specified as a directed acyclic graph, our method automatically derives valid factorizations of the joint posterior and matching neural network architectures. The key steps, graph expansion and graph inversion, yield an inverse graph that determines how inference networks are stacked and conditioned, producing...

    arxiv.org/abs/2609.40024 · PDF

  3. 03

    BayesNDE: Bayesian Generative Modeling for Neural Density Estimation

    Chenglin Li, Qiao Liu

    stat.ML · cs.AI · cs.LG · stat.ME

    Density estimation is a fundamental problem in statistics and machine learning. In this work, we introduce BayesNDE, a neural density estimator based on Bayesian generative modeling. BayesNDE learns a Bayesian generative model and evaluates its density without requiring invertible networks or Jacobian-determinant computation. For each observation, it infers a sample-specific latent posterior to construct an adaptive proposal that focuses...

    arxiv.org/abs/2609.39843 · PDF

  4. 04

    Estimation of the Label-Noise Transition Matrix with Performance Guarantees via Selective Classification

    Xabier de Juan, Santiago Mazuelas, Yilun Zhu, Clayton Scott

    stat.ML · cs.LG

    Modern machine learning depends heavily on massive datasets, but obtaining high-quality annotations at scale is often expensive. As a result, learning from noisily-labeled data has become common, making accurate estimation of the label-noise transition matrix crucial. However, existing transition matrix estimators rely on the fragile estimation of class-posteriors and do not provide finite-sample performance guarantees. In this work, we...

    arxiv.org/abs/2609.39829 · 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.