reproducing kernel hilbert space

Kernelized Wasserstein Natural Gradient ICLR 2020 Kernel Methods for Cooperative Multi-Agent Contextual Bandits ICML 2020 Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization AISTATS 2020 Learning Fair Representations for Kernel Models AISTATS 2020 Multi-Feedback Bandit Learning with Probabilistic Contexts IJCAI 2020 Non-asymptotic Analysis for Nonparametric Testing COLT 2020 On Mahalanobis Distance in Functional Settings JMLR 2020 On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels COLT 2020 On the Similarity between the Laplace and Neural Tangent Kernels NeurIPS 2020 Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms JMLR 2020 Robust Learning with the Hilbert-Schmidt Independence Criterion ICML 2020 Sample complexity and effective dimension for regression on manifolds NeurIPS 2020 Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms JMLR 2020 Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model NeurIPS 2020 When Do Neural Networks Outperform Kernel Methods? NeurIPS 2020 A Kernel Perspective for Regularizing Deep Neural Networks ICML 2019 A Representer Theorem for Deep Kernel Learning JMLR 2019 Autoencoding any Data through Kernel Autoencoders AISTATS 2019 Bayesian Optimization under Heavy-tailed Payoffs NeurIPS 2019 Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon COLT 2019 Continuous Direct Sparse Visual Odometry from RGB-D Images RSS 2019 Correlations between Word Vector Sets EMNLP 2019 Distribution-Based Semi-Supervised Learning for Activity Recognition AAAI 2019 Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations JMLR 2019 Infinite Task Learning in RKHSs AISTATS 2019