reproducing kernel hilbert space

KerGM: Kernelized Graph Matching NeurIPS 2019 Kernel Exponential Family Estimation via Doubly Dual Embedding AISTATS 2019 Kernel Instrumental Variable Regression NeurIPS 2019 Kernel Normalized Cut: a Theoretical Revisit ICML 2019 Kernel quadrature with DPPs NeurIPS 2019 Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration NeurIPS 2019 Online Learning in Kernelized Markov Decision Processes AISTATS 2019 Online learning with kernel losses ICML 2019 Recurrent Kernel Networks NeurIPS 2019 Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors AISTATS 2019 Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation CVPR 2018 Causal Inference via Kernel Deviance Measures NeurIPS 2018 Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces NeurIPS 2018 Convergence of Unregularized Online Learning Algorithms JMLR 2018 Differentially Private Database Release via Kernel Mean Embeddings ICML 2018 Early Stopping for Nonparametric Testing NeurIPS 2018 Fast Factorization-free Kernel Learning for Unlabeled Chunk Data Streams IJCAI 2018 Fast generalization error bound of deep learning from a kernel perspective AISTATS 2018 Goodness-of-Fit Testing for Discrete Distributions via Stein Discrepancy ICML 2018 Initialization matters: Orthogonal Predictive State Recurrent Neural Networks ICLR 2018 Kernel Conditional Exponential Family AISTATS 2018 Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions JMLR 2018 Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators NeurIPS 2018 Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods ICML 2018 Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces ICML 2018