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Kernel Methods

73 directly classified papers

Papers per year

Papers

Statistical Optimal Transport posed as Learning Kernel Embedding NIPS 2020
A Fair Classifier Using Kernel Density Estimation NIPS 2020
Sobolev Independence Criterion NIPS 2019
Kernel Instrumental Variable Regression NIPS 2019
Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing NIPS 2019
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration NIPS 2019
Space and Time Efficient Kernel Density Estimation in High Dimensions NIPS 2019
Kernel quadrature with DPPs NIPS 2019
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces NIPS 2018
On Fast Leverage Score Sampling and Optimal Learning NIPS 2018
Early Stopping for Nonparametric Testing NIPS 2018
Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels NIPS 2018
Quadrature-based features for kernel approximation NIPS 2018
Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning ACL 2017
Data Augmentation, Missing Feature Mask and Kernel Classification for Through-the-Wall Acoustic Surveillance INTERSPEECH 2017
Multi-Way Multi-Level Kernel Modeling for Neuroimaging Classification CVPR 2017
FALKON: An Optimal Large Scale Kernel Method NIPS 2017
Early stopping for kernel boosting algorithms: A general analysis with localized complexities NIPS 2017
Diving into the shallows: a computational perspective on large-scale shallow learning NIPS 2017
Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds CVPR 2016
Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification CVPR 2016
Dual Space Gradient Descent for Online Learning NIPS 2016
Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes NIPS 2016
When VLAD Met Hilbert CVPR 2016
A Stable Multi-Scale Kernel for Topological Machine Learning CVPR 2015