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Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit

, , and . (2019)cite arxiv:1902.06015Comment: 61 pages.

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Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality., , , and . COLT, volume 65 of Proceedings of Machine Learning Research, page 1476-1515. PMLR, (2017)Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit, , and . (2019)cite arxiv:1902.06015Comment: 61 pages.Limitations of Lazy Training of Two-layers Neural Networks, , , and . (2019)cite arxiv:1906.08899Comment: 39 pages; 2 pdf figures.The merged-staircase property: a necessary and nearly sufficient condition for SGD learning of sparse functions on two-layer neural networks., , and . COLT, volume 178 of Proceedings of Machine Learning Research, page 4782-4887. PMLR, (2022)Linearized two-layers neural networks in high dimension., , , and . CoRR, (2019)The merged-staircase property: a necessary and nearly sufficient condition for SGD learning of sparse functions on two-layer neural networks., , and . CoRR, (2022)Learning with convolution and pooling operations in kernel methods., and . NeurIPS, (2022)Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression., , , , and . NeurIPS, (2022)Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime., , and . CoRR, (2024)A non-asymptotic theory of Kernel Ridge Regression: deterministic equivalents, test error, and GCV estimator., and . CoRR, (2024)