Kernel Methods

Kernel Embeddings of Latent Tree Graphical Models NeurIPS 2011 Laplacian Support Vector Machines Trained in the Primal JMLR 2011 Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint NeurIPS 2011 Learning low-rank output kernels ACML 2011 Metric Learning with Multiple Kernels NeurIPS 2011 Microbagging Estimators: An Ensemble Approach to Distance-weighted Classifiers ACML 2011 Nonlinear Online Classification Algorithm with Probability Margin ACML 2011 Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels NeurIPS 2011 Online Learning of Structured Predictors with Multiple Kernels AISTATS 2011 Optimal learning rates for least squares SVMs using Gaussian kernels NeurIPS 2011 Reinforcement Learning using Kernel-Based Stochastic Factorization NeurIPS 2011 Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning NeurIPS 2011 The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers NeurIPS 2011 The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning NeurIPS 2011 Two-Layer Multiple Kernel Learning AISTATS 2011 Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning NeurIPS 2011 Unsupervised Multiple Kernel Learning ACML 2011 A Novel Kernel for Learning a Neuron Model from Spike Train Data NeurIPS 2010 Avoiding False Positive in Multi-Instance Learning NeurIPS 2010 Bayesian Generalized Kernel Models AISTATS 2010 Efficient algorithms for learning kernels from multiple similarity matrices with general convex loss functions NeurIPS 2010 Efficient Multioutput Gaussian Processes through Variational Inducing Kernels AISTATS 2010 Inductive Regularized Learning of Kernel Functions NeurIPS 2010 Kernel Descriptors for Visual Recognition NeurIPS 2010 Kernel Partial Least Squares is Universally Consistent AISTATS 2010