kernel methods

On the optimality of kernels for high-dimensional clustering AISTATS 2020 Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations NeurIPS 2020 pyDML: A Python Library for Distance Metric Learning JMLR 2020 Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel ICLR 2020 Random Fourier Features via Fast Surrogate Leverage Weighted Sampling AAAI 2020 Random Projections and α-Shape to Support the Kernel Design (Student Abstract) AAAI 2020 Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound ICML 2020 Rescuing neural spike train models from bad MLE NeurIPS 2020 Reservoir Computing meets Recurrent Kernels and Structured Transforms NeurIPS 2020 Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets CVPR 2020 Sample complexity and effective dimension for regression on manifolds NeurIPS 2020 Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase ICML 2020 SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical Kernel CVPR 2020 Select to Better Learn: Fast and Accurate Deep Learning Using Data Selection From Nonlinear Manifolds CVPR 2020 Sequential Recommendation with Relation-Aware Kernelized Self-Attention AAAI 2020 Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms JMLR 2020 Sparse Hilbert-Schmidt Independence Criterion Regression AISTATS 2020 Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning NeurIPS 2020 Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes NeurIPS 2020 SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence NeurIPS 2020 Testing Goodness of Fit of Conditional Density Models with Kernels UAI 2020 The Statistical Complexity of Early-Stopped Mirror Descent NeurIPS 2020 The Statistical Cost of Robust Kernel Hyperparameter Turning NeurIPS 2020 The weight function in the subtree kernel is decisive JMLR 2020 Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model NeurIPS 2020