kernel methods

Efficient Structure-preserving Support Tensor Train Machine JMLR 2023 Error Estimation for Random Fourier Features AISTATS 2023 Estimating Koopman operators with sketching to provably learn large scale dynamical systems NeurIPS 2023 Estimation Beyond Data Reweighting: Kernel Method of Moments ICML 2023 Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods ICLR 2023 Expanding the Hyperbolic Kernels: A Curvature-aware Isometric Embedding View IJCAI 2023 Fast and scalable score-based kernel calibration tests UAI 2023 Federated Spectral Clustering via Secure Similarity Reconstruction NeurIPS 2023 Few-Sample Feature Selection via Feature Manifold Learning ICML 2023 Functional Renyi Differential Privacy for Generative Modeling NeurIPS 2023 GAUCHE: A Library for Gaussian Processes in Chemistry NeurIPS 2023 Gaussian Processes on Distributions based on Regularized Optimal Transport AISTATS 2023 Gold Doesn’t Always Glitter: Spectral Removal of Linear and Nonlinear Guarded Attribute Information EACL 2023 Gradient Boosting Performs Gaussian Process Inference ICLR 2023 Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space ICML 2023 Gradient-Free Kernel Stein Discrepancy NeurIPS 2023 Graph-Aided Online Multi-Kernel Learning JMLR 2023 Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning ICLR 2023 Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems AISTATS 2023 Integrating Prior Knowledge in Contrastive Learning with Kernel ICML 2023 Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions NeurIPS 2023 Isotropic Gaussian Processes on Finite Spaces of Graphs AISTATS 2023 Kernel-Based Tests for Likelihood-Free Hypothesis Testing NeurIPS 2023 Kernel Conditional Moment Constraints for Confounding Robust Inference AISTATS 2023 Kernelized Cumulants: Beyond Kernel Mean Embeddings NeurIPS 2023