stat.ML · 2026-07-30 · No. 69
Machine Learning, 2026-07-30.
9 new papers in stat.ML. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
9 entries-
01
PIKS: Universal Physics-Informed Kernel Methods
Joachim Bona-Pellissier, Giacomo Meanti, Matteo Santacesaria, Lorenzo Rosasco
stat.ML · cs.LG
Physics-informed machine learning incorporates physical principles --often expressed via differential operators-- into data-driven models. While physics-informed neural networks (PINNs) dominate empirical applications, the complexity of neural network architectures and optimization landscapes hinders the development of a corresponding learning theory. In turn, kernel methods offer an appealing alternative with closed-form solutions and...
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02
Feature Bagging Provides Stability
Yuheng Ma, Qiang Sun
stat.ML · cs.LG · math.ST
We study feature bagging through the lens of algorithmic stability. Feature bagging is an ensemble strategy that aggregates base learners trained on randomly subsampled feature subsets, possibly in a data-dependent manner. We introduce feature instability (FI), the feature-axis analogue of instance instability (II), which measures sensitivity to removing a single feature. Smaller values of II or FI correspond to stronger stability, and our...
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03
Breaking the Curse with BAND: Nonparametric Distribution Estimation in High Dimensions
Shuo-Chieh Huang, Chien-Ming Chi, Jau-er Chen
stat.ML · cs.LG · stat.ME
Minimax-optimal rates for multivariate distribution estimation are known to suffer from the curse of dimensionality. We propose a sparse Bayesian network approach in which each conditional probability is estimated using sparsity-aware conditional mean methods. The resulting estimator, \textit{BAyesian Network Distribution regression} (BAND), handles mixed data types in high-dimensional time series and achieves polynomial total variation...
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04
Conformalized Rate-Adaptive Sensing
Jiawei Yang, Yao Zhang
stat.ML · cs.LG · stat.AP · stat.ME
Many high-resolution imaging systems face the same fundamental question: when have enough measurements been collected to reconstruct an image accurately? We develop Conformalized Rate-Adaptive Sensing (CoRAS), a method that adaptively chooses an acquisition or compression rate for each image while keeping the reconstruction error below a target level with high probability. As measurements are collected, an image reconstruction model gradually...
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05
Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents
Amirmohammad Farzaneh, Osvaldo Simeone
stat.ML · cs.AI · cs.IT · cs.LG
LLM agents following the ReAct paradigm are promising enablers of complex multi-step tasks, including multi-hop question answering, code generation, and control of physical AI systems. Yet, when deployed at the edge, they must tightly manage their reasoning budget while remaining reliable and deferring to a cloud-side model only when local uncertainty is too high to act safely. We propose Think Short, Defer Smart (TSDS), a framework that...
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06
Crossing-Free Probabilistic K-Line Forecasts Without Retraining
Runyao Yu, Yuchen Tao, Yujie Chen, Wentao Wang, Derek W. Bunn
stat.ML · cs.AI · cs.CE · cs.LG · q-fin.CP
Probabilistic K-line forecasting describes uncertainty in four complementary prices, namely open--high--low--close (OHLC). However, it introduces two consistency problems: quantile crossing and K-line crossing. Quantile crossing occurs when a higher-quantile forecast falls below a lower-quantile forecast, while K-line crossing occurs when the forecast low exceeds the open or close, or the forecast high falls below the open or close. Existing...
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07
Early Failure Prediction from Near-Anomaly Detection: A Proactive Approach
Léa Billet, Louise Travé-Massuyès, Elodie Chanthery, Alexandre Gaffet
stat.ML · cs.LG
Anomaly detection methods often have uncertain behavior with respect to samples near the distribution boundary, limiting their ability to anticipate future anomalies. This work introduces the concept of near-anomalies that, while not yet anomalous, lie close to the boundary and are likely to transition into anomalies in the near future. To address this, we propose an unsupervised method, named Christoffel-based ANomaly Anticipation for eaRly...
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08
Chaos Is a LADDER: Domain Generalization Beyond Invariance via Reweighting
Yuhang Jiang, Fengchuan Zhang, Sanguo Zhang, Guojun Zhu
stat.ML · cs.LG · math.ST · stat.ME
Domain generalization (DG) aims to learn from multiple source domains and generalize to unseen target domains. Most DG methods pursue invariance: they seek a causal representation whose prediction rule is invariant across domains. This principle is effective when the causal mechanism is stable, but becomes restrictive when the domain itself modulates how causal content maps to the response. In this case, directly feeding domain style into the...
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09
Origins and mitigation of double descent in reduced order modeling
Andrei A. Klishin, J. Nathan Kutz, Krithika Manohar
stat.ML · cs.LG · math.DS · physics.data-an
Latent low-dimensional structure in datasets of natural and engineered systems enables their sparse sensing, or full-state reconstruction from historical data and very few carefully chosen localized measurements. Depending on the reconstruction algorithm, sensor locations, and measurement noise, the reconstruction risk curves demonstrate a diversity of patterns including a dramatic peak in error known as double descent in Machine Learning...
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