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Kernel Methods

96 directly classified papers

Papers per year

Papers

Learning and Inference in Hilbert Space with Quantum Graphical Models NIPS 2018
Non-Stationary Spectral Kernels NIPS 2017
Minimax Estimation of Kernel Mean Embeddings JMLR 2017
Multi-Way Multi-Level Kernel Modeling for Neuroimaging Classification CVPR 2017
Distributed Learning with Regularized Least Squares JMLR 2017
Recursive Sampling for the Nystrom Method NIPS 2017
Data Augmentation, Missing Feature Mask and Kernel Classification for Through-the-Wall Acoustic Surveillance INTERSPEECH 2017
Kernel Feature Selection via Conditional Covariance Minimization NIPS 2017
Generative Local Metric Learning for Kernel Regression NIPS 2017
Spectral Mixture Kernels for Multi-Output Gaussian Processes NIPS 2017
Consistent Kernel Mean Estimation for Functions of Random Variables NIPS 2016
Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning NIPS 2016
On Robustness of Kernel Clustering NIPS 2016
Error Analysis of Generalized Nyström Kernel Regression NIPS 2016
Modelling Policies in MDPs in Reproducing Kernel Hilbert Space AISTATS 2015
Riemannian Coding and Dictionary Learning: Kernels to the Rescue CVPR 2015
Learning with Group Invariant Features: A Kernel Perspective. NIPS 2015
Statistical Topological Data Analysis - A Kernel Perspective NIPS 2015
Spherical Random Features for Polynomial Kernels NIPS 2015
Kernel Mean Estimation via Spectral Filtering NIPS 2014
Scalable Kernel Methods via Doubly Stochastic Gradients NIPS 2014
q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions NIPS 2013
JKernelMachines: A Simple Framework for Kernel Machines JMLR 2013
Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. NIPS 2013
Robust Low Rank Kernel Embeddings of Multivariate Distributions NIPS 2013