Kernel Methods

When VLAD Met Hilbert CVPR 2016 A Stable Multi-Scale Kernel for Topological Machine Learning CVPR 2015 Geodesic Exponential Kernels: When Curvature and Linearity Conflict CVPR 2015 Modelling Policies in MDPs in Reproducing Kernel Hilbert Space AISTATS 2015 Two-stage sampled learning theory on distributions AISTATS 2015 Fast Kernel Learning for Multidimensional Pattern Extrapolation NeurIPS 2014 Human Action Recognition Based on Context-Dependent Graph Kernels CVPR 2014 Adaptivity to Local Smoothness and Dimension in Kernel Regression NeurIPS 2013 Inverse Density as an Inverse Problem: the Fredholm Equation Approach NeurIPS 2013 JKernelMachines: A Simple Framework for Kernel Machines JMLR 2013 Kernel Hyperalignment NeurIPS 2012 The representer theorem for Hilbert spaces: a necessary and sufficient condition NeurIPS 2012 Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint NeurIPS 2011 Efficient algorithms for learning kernels from multiple similarity matrices with general convex loss functions NeurIPS 2010 A Fast, Consistent Kernel Two-Sample Test NeurIPS 2009 Fast subtree kernels on graphs NeurIPS 2009 Learning Non-Linear Combinations of Kernels NeurIPS 2009 Bayesian Network Score Approximation using a Metagraph Kernel NeurIPS 2008 Deep Learning with Kernel Regularization for Visual Recognition NeurIPS 2008 Diffeomorphic Dimensionality Reduction NeurIPS 2008 Kernel Measures of Conditional Dependence NeurIPS 2007 Active learning for misspecified generalized linear models NeurIPS 2006 A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems NeurIPS 2006 Gaussian and Wishart Hyperkernels NeurIPS 2006 Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space NeurIPS 2006