arXiv Machine Learning July 27, 2026 By Gil Kur, Pierre Bizeul

Minimum Norm Interpolation via the Local Theory of Banach Spaces: The Role of $2$-Uniform Convexity

Read the original on arXiv Machine Learning →

arXiv:2603. 28956v2 Announce Type: replace-cross Abstract: The minimum-norm interpolator (MNI) framework has recently attracted considerable attention as a tool for understanding generalization in overparameterized models, such as neural networks.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv Machine Learning

Jun 30

Generalization error of min-norm interpolators in transfer learning

arXiv:2406. 13944v2 Announce Type: replace-cross Abstract: This paper establishes the generalization error of pooled min-$\ell_2$-norm interpolation in transfer learning, where data from diverse distributions are available.

By Yanke Song, Kenneth Gu, Sohom Bhattacharya, Pragya Sur

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arXiv Machine Learning

Jul 15

Learning and Testing Convex Functions

arXiv:2511. 11498v2 Announce Type: replace-cross Abstract: We consider the problems of \emph{learning} and \emph{testing} real-valued convex functions over Gaussian space.

By Renato Ferreira Pinto Jr., Cassandra Marcussen, Elchanan Mossel, Shivam Nadimpalli

arXiv Machine Learning

Jun 19

Benign overfitting beyond prediction: The ordinary least squares interpolator

arXiv:2309. 15769v3 Announce Type: replace-cross Abstract: Recent advances in deep learning have highlighted the phenomenon of benign overfitting in overparameterized statistical models, sparking significant interest in understanding its foundations.

By Dennis Shen, Dogyoon Song, Peng Ding, Jasjeet S. Sekhon

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arXiv Machine Learning

Jun 30

Replica Symmetry Breaking and Algorithmic Thresholds in Empirical Risk Minimization under Multi-Index Model

arXiv:2606. 28573v1 Announce Type: new Abstract: Modern machine learning models are trained by optimizing high-dimensional non-convex empirical risk functions.

By Andrea Montanari, Kangjie Zhou

arXiv Machine Learning

Aug 12

Dual Space Preconditioning for Gradient Descent in the Overparameterized Regime

arXiv:2603. 10485v3 Announce Type: replace-cross Abstract: In this work, we study the convergence properties of the Dual Space Preconditioned Gradient Descent, encompassing optimizers such as Normalized Gradient Descent and Gradient Clipping.

By Reza Ghane, Danil Akhtiamov, Babak Hassibi

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arXiv AI

1d ago

Optimal Lower Bounds for Networked Information Aggregation

arXiv:2608. 15472v1 Announce Type: cross Abstract: The problem of networked information aggregation, studied in Kearns et al.

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