stat.ML · 2026-06-23 · No. 32
Machine Learning, 2026-06-23.
7 new papers in stat.ML. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
7 entries-
01
Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives
Tom Rossa, Angus Phillips, Tom Rainforth
stat.ML · cs.LG
Bayesian experimental design (BED) has traditionally been based on maximising expected uncertainty reductions from prior to posterior. A major shortfall of this approach is that it leads to doubly intractable objectives that are difficult to optimise, while customising them to particular downstream tasks of interest can also be difficult. Following first principles decision theory, we demonstrate that BED can alternatively be formulated in...
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02
Diffusion Models Adapt to Low-Dimensional Structure Under Flexible Coefficient Choices
Changxiao Cai, Yuchen Jiao, Gen Li
stat.ML · cs.LG · math.ST
Diffusion models are known to exploit unknown low-dimensional structure to accelerate sampling. However, existing convergence theory under low-dimensional data structure has largely focused on update rules with narrowly prescribed coefficient choices. This raises a fundamental question: is adaptation to low-dimensional structure sensitive to the precise choice of update coefficients? In this paper, we show that such adaptation is a robust...
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03
Neural Networks as Linear Regression: An Introduction for Statisticians
Abigail Loe, Susan Murray, Zhenke Wu
stat.ML · cs.LG
Neural networks are a commonly used prediction tool in computer science and statistics. However, the barrier to entry of this interesting field remains high, particularly for classical statisticians trained in a frequentist perspective. In this letter, we demystify neural networks by describing networks that approximate a linear regression and describe common customizations that provide a foundation for further study.
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04
FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data
Marcel Hedman, Emily Alger, Brieuc Lehmann, Chris Holmes, Tom Rainforth
stat.ML · cs.LG
Frameworks for ensuring fairness in machine learning typically focus on learning fair models from existing data. But this endeavor is often undermined by biases already present in that data. We therefore look to modify the data acquisition process itself to help gather fairer data that is inherently more suitable for training fair predictors. To this end, we introduce FairBED, which provides novel formulations for quantifying the fairness of...
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05
Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions
Sehwan Kim, Yan Sun, Faming Liang
stat.ML · cs.LG · math.ST
Over the past decade, deep neural networks (DNNs) have achieved remarkable success on complex machine-learning tasks, yet the theoretical foundations of their performance remain incomplete. From a statistical viewpoint, a natural question is: can DNNs attain feature-learning and prediction consistency comparable to that of classical models? While a full characterization is open, we provide positive results for a broad subclass. We establish...
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06
Time Series Classification through Diffeomorphic Time Warping (DiffTW)
Vicky Geneva Haney, Kamel Lahouel, Victor Rielly, Bruno M. Jedynak
stat.ML · cs.LG
Time series classification involves learning a mapping from a continuous, temporally ordered sequence of real-valued observations to a discrete response variable, like class labels. This task is fundamental in domains, including health monitoring, where the temporal structure of data is critical for accurate prediction. Dynamic Time Warping (DTW) is a standard technique for measuring similarity between sequences varying in time or speed....
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07
Domain Adaptation Under Wireless Network Constraints: When Does It Become Green?
Illyyne Saffar, Aurélie Boisbunon, Shruti Bothe
stat.ML · cs.AI · cs.LG
The deployment of data-driven models in 6G wireless networks is increasingly challenged by frequent distribution shifts that degrade performance over time. Unsupervised Domain Adaptation (UDA) offers an alternative approach by adapting the trained model to a shifted domain without requiring labels. However, UDA pipelines are often more complex than single-task training due to additional modules and optimization procedures, raising a practical...
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