kernel methods

Regularized ERM on random subspaces AISTATS 2021 Scaling Neural Tangent Kernels via Sketching and Random Features NeurIPS 2021 Self-Supervised Learning with Kernel Dependence Maximization NeurIPS 2021 Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics NeurIPS 2021 Structure-Aware Random Fourier Kernel for Graphs NeurIPS 2021 Structured Convolutional Kernel Networks for Airline Crew Scheduling ICML 2021 Testing Independence Between Linear Combinations for Causal Discovery AAAI 2021 The Recurrent Neural Tangent Kernel ICLR 2021 The Teaching Dimension of Kernel Perceptron AISTATS 2021 Towards a Unified Analysis of Random Fourier Features JMLR 2021 Understanding Bandits with Graph Feedback NeurIPS 2021 Understanding Recurrent Neural Networks Using Nonequilibrium Response Theory JMLR 2021 Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels NeurIPS 2021 A Cluster-Weighted Kernel K-Means Method for Multi-View Clustering AAAI 2020 A Graduated Filter Method for Large Scale Robust Estimation CVPR 2020 A Linear-time Independence Criterion Based on a Finite Basis Approximation AISTATS 2020 A Non-Asymptotic Analysis for Stein Variational Gradient Descent NeurIPS 2020 Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity COLT 2020 Approximate Representer Theorems in Non-reflexive Banach Spaces ALT 2020 A Quantum-inspired Entropic Kernel for Multiple Financial Time Series Analysis IJCAI 2020 A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent NeurIPS 2020 A Stein Goodness-of-fit Test for Directional Distributions AISTATS 2020 A Three-Level Optimization Model for Nonlinearly Separable Clustering AAAI 2020 Automated Spectral Kernel Learning AAAI 2020 Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach L4DC 2020