kernel approximation

The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings NeurIPS 2017 Dual Space Gradient Descent for Online Learning NeurIPS 2016 Kernel Approximation via Empirical Orthogonal Decomposition for Unsupervised Feature Learning CVPR 2016 Large Scale Online Kernel Learning JMLR 2016 Object Tracking via Dual Linear Structured SVM and Explicit Feature Map CVPR 2016 Orthogonal Random Features NeurIPS 2016 Practical Kernel-Based Reinforcement Learning JMLR 2016 Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels JMLR 2016 Random Features for Sparse Signal Classification CVPR 2016 Recycling Randomness with Structure for Sublinear time Kernel Expansions ICML 2016 Basis Mapping Based Boosting for Object Detection CVPR 2015 Double Nyström Method: An Efficient and Accurate Nyström Scheme for Large-Scale Data Sets ICML 2015 Hilbert maps: scalable continuous occupancy mapping with stochastic gradient descent RSS 2015 Learning with Group Invariant Features: A Kernel Perspective. NeurIPS 2015 Low Dimensional Explicit Feature Maps ICCV 2015 Optimal Rates for Random Fourier Features NeurIPS 2015 Spherical Random Features for Polynomial Kernels NeurIPS 2015 Asymmetric Sparse Kernel Approximations for Large-scale Visual Search CVPR 2014 Compact Random Feature Maps ICML 2014 Fast Prediction for Large-Scale Kernel Machines NeurIPS 2014 Hash-SVM: Scalable Kernel Machines for Large-Scale Visual Classification CVPR 2014 Memory Efficient Kernel Approximation ICML 2014 Random Laplace Feature Maps for Semigroup Kernels on Histograms CVPR 2014 Support vector machines with indefinite kernels ACML 2014 A Framework for Evaluating Approximation Methods for Gaussian Process Regression JMLR 2013