Kernel Methods

Random Fourier Features via Fast Surrogate Leverage Weighted Sampling AAAI 2020 Reservoir Computing meets Recurrent Kernels and Structured Transforms NeurIPS 2020 Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms JMLR 2020 Statistical Optimal Transport posed as Learning Kernel Embedding NeurIPS 2020 Comparing distributions: ℓ_1 geometry improves kernel two-sample testing NeurIPS 2019 Kernel Instrumental Variable Regression NeurIPS 2019 Kernel quadrature with DPPs NeurIPS 2019 Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration NeurIPS 2019 Sobolev Independence Criterion NeurIPS 2019 Space and Time Efficient Kernel Density Estimation in High Dimensions NeurIPS 2019 Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces NeurIPS 2018 Early Stopping for Nonparametric Testing NeurIPS 2018 Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels NeurIPS 2018 On Fast Leverage Score Sampling and Optimal Learning NeurIPS 2018 Quadrature-based features for kernel approximation NeurIPS 2018 Data Augmentation, Missing Feature Mask and Kernel Classification for Through-the-Wall Acoustic Surveillance Interspeech 2017 Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning ACL 2017 Diving into the shallows: a computational perspective on large-scale shallow learning NeurIPS 2017 Early stopping for kernel boosting algorithms: A general analysis with localized complexities NeurIPS 2017 FALKON: An Optimal Large Scale Kernel Method NeurIPS 2017 Multi-Way Multi-Level Kernel Modeling for Neuroimaging Classification CVPR 2017 Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification CVPR 2016 Dual Space Gradient Descent for Online Learning NeurIPS 2016 Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes NeurIPS 2016 Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds CVPR 2016