random feature

Decentralised Learning with Random Features and Distributed Gradient Descent ICML 2020 Generalisation error in learning with random features and the hidden manifold model ICML 2020 Generalized Leverage Score Sampling for Neural Networks NeurIPS 2020 Graph Random Neural Features for Distance-Preserving Graph Representations ICML 2020 Learning to Learn Kernels with Variational Random Features ICML 2020 Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions NeurIPS 2020 Reservoir Computing meets Recurrent Kernels and Structured Transforms NeurIPS 2020 Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition NeurIPS 2020 On the Power and Limitations of Random Features for Understanding Neural Networks NeurIPS 2019 Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization IJCAI 2019 Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics JMLR 2019 But How Does It Work in Theory? Linear SVM with Random Features NeurIPS 2018 Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels NeurIPS 2018 Learning with SGD and Random Features NeurIPS 2018 Not-So-Random Features ICLR 2018 On the Spectrum of Random Features Maps of High Dimensional Data ICML 2018 Quadrature-based features for kernel approximation NeurIPS 2018 Random Warping Series: A Random Features Method for Time-Series Embedding AISTATS 2018 Relating Leverage Scores and Density using Regularized Christoffel Functions NeurIPS 2018 An Empirical Study on The Properties of Random Bases for Kernel Methods NeurIPS 2017 Generalization Properties of Learning with Random Features NeurIPS 2017 Large-scale Online Kernel Learning with Random Feature Reparameterization IJCAI 2017 Learning Infinite Layer Networks Without the Kernel Trick ICML 2017 On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions JMLR 2017 Random Feature Expansions for Deep Gaussian Processes ICML 2017