Kernel Methods

Consistent Kernel Mean Estimation for Functions of Random Variables NeurIPS 2016 Cost Sensitive Online Multiple Kernel Classification ACML 2016 Discriminative Invariant Kernel Features: A Bells-and-Whistles-Free Approach to Unsupervised Face Recognition and Pose Estimation CVPR 2016 Dual Space Gradient Descent for Online Learning NeurIPS 2016 Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis NeurIPS 2016 Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold CVPR 2016 Error Analysis of Generalized Nyström Kernel Regression NeurIPS 2016 Examples are not enough, learn to criticize! Criticism for Interpretability NeurIPS 2016 Fast DPP Sampling for Nystrom with Application to Kernel Methods ICML 2016 Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels JMLR 2016 Interpretable Distribution Features with Maximum Testing Power NeurIPS 2016 Kernel Approximation via Empirical Orthogonal Decomposition for Unsupervised Feature Learning CVPR 2016 Kernel Mean Shrinkage Estimators JMLR 2016 Large Scale Online Kernel Learning JMLR 2016 Learning Theory for Distribution Regression JMLR 2016 Low-Rank Regression with Tensor Responses NeurIPS 2016 Megaman: Scalable Manifold Learning in Python JMLR 2016 Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels NeurIPS 2016 Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning NeurIPS 2016 Multiple Kernel Learning with Data Augmentation ACML 2016 On Quantile Regression in Reproducing Kernel Hilbert Spaces with the Data Sparsity Constraint JMLR 2016 On Robustness of Kernel Clustering NeurIPS 2016 On Valid Optimal Assignment Kernels and Applications to Graph Classification NeurIPS 2016 Optimal Learning Rates for Localized SVMs JMLR 2016 Orthogonal Random Features NeurIPS 2016