kernel approximation

Random Fourier Features via Fast Surrogate Leverage Weighted Sampling AAAI 2020 Bayesian Optimization under Heavy-tailed Payoffs NeurIPS 2019 Data-dependent compression of random features for large-scale kernel approximation AISTATS 2019 Kernel Approximation Methods for Speech Recognition JMLR 2019 Learning Adaptive Random Features AAAI 2019 Low-Precision Random Fourier Features for Memory-constrained Kernel Approximation AISTATS 2019 Periodic Kernel Approximation by Index Set Fourier Series Features UAI 2019 Unifying Orthogonal Monte Carlo Methods ICML 2019 Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features NeurIPS 2018 Quadrature-based features for kernel approximation NeurIPS 2018 Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF) ICML 2018 The Geometry of Random Features AISTATS 2018 Data-driven Random Fourier Features using Stein Effect IJCAI 2017 Distributed Adaptive Sampling for Kernel Matrix Approximation AISTATS 2017 Gaussian Quadrature for Kernel Features NeurIPS 2017 Large-Scale Data-Dependent Kernel Approximation AISTATS 2017 Large-scale Online Kernel Learning with Random Feature Reparameterization IJCAI 2017 Local Group Invariant Representations via Orbit Embeddings AISTATS 2017 Memory Efficient Kernel Approximation JMLR 2017 Multiresolution Kernel Approximation for Gaussian Process Regression NeurIPS 2017 Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions NeurIPS 2017 Non-parametric Policy Search with Limited Information Loss JMLR 2017 Recursive Sampling for the Nystrom Method NeurIPS 2017 Spherical Structured Feature Maps for Kernel Approximation ICML 2017 Structured adaptive and random spinners for fast machine learning computations AISTATS 2017