stat.ML · 2026-08-17 · No. 87

Machine Learning, 2026-08-17.

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

    Online Inference in Distributional Temporal-Difference Learning

    Yang Peng, Liangyu Zhang

    stat.ML · cs.LG

    We study online statistical inference for functionals of the return distribution under a fixed policy. The return distribution is estimated by nonparametric distributional temporal-difference learning from a single Markov trajectory. For the Polyak--Ruppert averaged estimator, we prove that its root-$T$ error converges weakly to a centered Gaussian random element in Cramér space. We also prove that, conditionally on the observed trajectory,...

    arxiv.org/abs/2608.14408 · PDF

  2. 02

    Offline Deep Q* Estimation with Diffusion Models

    Xiaohong Chen, Yuling Jiao, Lican Kang, Jerry Zhijian Yang, Chen Zhong

    stat.ML · cs.LG

    In offline RL, estimating the optimal action-value function $Q^*$ can be formulated as solving the optimal Bellman equation based solely on offline observations. A fundamental challenge is that the reward function and transition kernel are unknown, so the optimal Bellman operator is not directly observable from data. To address this issue, we propose a novel framework that decouples operator estimation from value function learning. In this...

    arxiv.org/abs/2608.14401 · PDF

  3. 03

    Deep Vision in Smart Manufacturing: MODERN Framework for Intelligent Quality Monitoring and Diagnosis

    Yicheng Kang, Yuling Jiao, Xin Geng, Mahesh Nagarajan

    stat.ML · cs.LG

    Smart manufacturing processes are often installed with a large number of sensors, imaging devices and computers, which not only enable instant communication across various modules of a production system but also aid in intelligent manufacturing management. In this paper, we introduce MODERN, a deep learning framework for quality monitoring and fault isolation, which integrates these enhanced capabilities into the practice of industrial...

    arxiv.org/abs/2608.13937 · PDF

  4. 04

    On the Brittleness of Maximum Likelihood Estimation for Gaussian Process Hyperparameter Optimization

    Tyler R. Johnson, Kian Ben-Jacob, Christopher P. Muller, Ramin Bostanabad

    stat.ML · cs.LG · stat.ME

    Machine learning (ML) has become an indispensable part of modern engineering design workflows. A crucial step in training an ML model is the selection of the loss function which can be systematically formulated via various techniques such as maximum likelihood estimation (MLE) and cross-validation . While MLE is one of the most popular, effective, and intuitive mechanisms for training ML models, it is brittle: if the assumptions underpinning...

    arxiv.org/abs/2608.13793 · PDF

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