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cs.LG2026
Constructive Universal Approximation by Infinite-Dimensional Neural Networks via Gaussian-Weighted Ridgelet Transform
Sho Sonoda, Luca Galimberti, Yuanchao Xu +2
Neural networks whose inputs are functions, as in operator learning, are usually shown to be universal by existence arguments, either by reduction to finite-dimensional networks or…
stat.ML2026
High-Probability Guarantees for SGD under -Heavy-Tailed Gradient Noise
Qijun Tong, Masahiro Ikeda, Ryota Kawasumi
Stochastic gradient descent (SGD) is widely used to train machine learning models, but subsampling the training data introduces noise into its updates. The strength and applicabili…