eigenvalue decay

On the Benefits of Active Data Collection in Operator Learning ICML 2025 Dimension-free deterministic equivalents and scaling laws for random feature regression NeurIPS 2024 On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains JMLR 2024 On the Optimality of Misspecified Spectral Algorithms JMLR 2024 Characterizing the spectrum of the NTK via a power series expansion ICLR 2023 Improved Kernel Alignment Regret Bound for Online Kernel Learning AAAI 2023 Nearly Optimal Algorithms with Sublinear Computational Complexity for Online Kernel Regression ICML 2023 Exploring the Gap between Collapsed & Whitened Features in Self-Supervised Learning ICML 2022 On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels NeurIPS 2022 A Spectral Analysis of Dot-product Kernels AISTATS 2021 Deep Equals Shallow for ReLU Networks in Kernel Regimes ICLR 2021 How rotational invariance of common kernels prevents generalization in high dimensions ICML 2021 On the Similarity between the Laplace and Neural Tangent Kernels NeurIPS 2020 Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes NeurIPS 2018 Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks NeurIPS 2017 Nonnegative Sparse PCA with Provable Guarantees ICML 2014 Sparse PCA through Low-rank Approximations ICML 2013 The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning NeurIPS 2011 On the Performance of Kernel Classes JMLR 2003