conftrace_

random feature

89 papers

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Also known as

RF

Co-occurring keywords

kernel methods (1097) kernel approximation (100) neural tangent kernel (205) neural network (6616) feature learning (1311) random feature approximation (13) double descent (52) stochastic gradient descent (1091) kernel regression (87) ridge regression (134)

Papers

Generalisation error in learning with random features and the hidden manifold model ICML 2020
Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition NIPS 2020
Graph Random Neural Features for Distance-Preserving Graph Representations ICML 2020
Decentralised Learning with Random Features and Distributed Gradient Descent 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 NIPS 2020
Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics JMLR 2019
On the Power and Limitations of Random Features for Understanding Neural Networks NIPS 2019
Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization IJCAI 2019
Learning with SGD and Random Features NIPS 2018
Random Warping Series: A Random Features Method for Time-Series Embedding AISTATS 2018
On the Spectrum of Random Features Maps of High Dimensional Data ICML 2018
But How Does It Work in Theory? Linear SVM with Random Features NIPS 2018
Quadrature-based features for kernel approximation NIPS 2018
Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels NIPS 2018
Relating Leverage Scores and Density using Regularized Christoffel Functions NIPS 2018
Random Feature Expansions for Deep Gaussian Processes ICML 2017
An Empirical Study on The Properties of Random Bases for Kernel Methods NIPS 2017
Generalization Properties of Learning with Random Features NIPS 2017
On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions JMLR 2017
Large-scale Online Kernel Learning with Random Feature Reparameterization IJCAI 2017
Learning Infinite Layer Networks Without the Kernel Trick ICML 2017
Recycling Randomness with Structure for Sublinear time Kernel Expansions ICML 2016
Dual Space Gradient Descent for Online Learning NIPS 2016
Kernel Approximation via Empirical Orthogonal Decomposition for Unsupervised Feature Learning CVPR 2016