arXiv Machine Learning June 16, 2026 By Siyu Chen, Beining Wu, Miao Lu, Zhuoran Yang, Tianhao Wang

Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model

Read the original on arXiv Machine Learning →

arXiv:2606. 15219v1 Announce Type: new Abstract: In this work, we tackle the following question: Can neural networks trained with gradient-based methods achieve the optimal computational-statistical tradeoff in learning Gaussian single-index models?

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efficiency

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

Jun 3

Optimal Rates for Generalization of Gradient Descent for Deep ReLU Classification

arXiv:2510. 02779v4 Announce Type: replace Abstract: Recent advances have significantly improved our understanding of the generalization performance of gradient descent (GD) methods in deep neural networks.

By Yuanfan Li, Yunwen Lei, Zheng-Chu Guo, Yiming Ying

arXiv Machine Learning

Jun 26

Fast algorithms for learning a Gaussian under halfspace truncation with optimal sample complexity

arXiv:2606. 27298v1 Announce Type: cross Abstract: We study the fundamental problem of learning a high-dimensional Gaussian truncated to an unknown halfspace.

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arXiv Machine Learning

Jun 9

Full-Batch Gradient Descent Outperforms One-Pass SGD: Sample Complexity Separation in Single-Index Learning

arXiv:2602. 02431v2 Announce Type: replace-cross Abstract: It is folklore that reusing training data more than once can improve the statistical efficiency of gradient-based learning.

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arXiv Machine Learning

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Scale-Invariant Neural Network Optimization: Norm Geometry and Heavy-Tailed Noise

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By Jiayu Zhang, Tianyi Lin

arXiv Machine Learning

Jun 17

Tight $L_\infty$ Sample Complexity for Low-Degree and Sparse Boolean Polynomials

arXiv:2606. 17319v1 Announce Type: cross Abstract: Motivated by the optimization of bounded binary black-box functions, we study the problem of learning polynomial surrogates over the Boolean hypercube.

By Jasper van Doornmalen, Mathieu Molina, Victor Verdugo, Jos\'e Verschae

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

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