Infinite-dimensional GAL can learn the invariant distribution of sufficiently chaotic dynamical systems from a single deterministic time series with explicit JS divergence convergence rates.
Shalev-Shwartz and S
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A certified adaptive quadrature framework computes guaranteed L^p, W^{1,p}, and W^{2,p} norms of deep neural networks by propagating interval enclosures on axis-aligned boxes.
Proximal stochastic spectral preconditioning converges for nonconvex constrained objectives under heavy-tailed noise, with a variance-reduced version achieving faster rates and a refined analysis of Muon iterations.
citing papers explorer
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Generative Adversarial Learning from Deterministic Processes
Infinite-dimensional GAL can learn the invariant distribution of sufficiently chaotic dynamical systems from a single deterministic time series with explicit JS divergence convergence rates.
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Certified and accurate computation of function space norms of deep neural networks
A certified adaptive quadrature framework computes guaranteed L^p, W^{1,p}, and W^{2,p} norms of deep neural networks by propagating interval enclosures on axis-aligned boxes.
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Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives
Proximal stochastic spectral preconditioning converges for nonconvex constrained objectives under heavy-tailed noise, with a variance-reduced version achieving faster rates and a refined analysis of Muon iterations.