Kernel Methods

A comprehensive benchmark of graph neural networks, graph kernels, and classical machine learning approaches on rs-fMRI brain graphs MIDL 2026 Adaptive Hyperbolic Kernels: Modulated Embedding in de Branges-Rovnyak Spaces AAAI 2026 A Martingale Kernel Two-Sample Test ALT 2026 Bandwidth Selection in Kernel Density Estimation for Model Calibration UAI 2026 Benign Overfitting with Quantum Kernels UAI 2026 Continuous time policy evaluation is easier with noisy dynamics COLT 2026 Eigen-Spike Emergence and Quadratic Equivalents for Conjugate Kernels on Nonlinearly Separable Data COLT 2026 Event Stream Filtering via Probability Flux Estimation CVPR 2026 Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables UAI 2026 Improving TensorSketch Using Complex Random Variables UAI 2026 Kernel-based Distributed Learning JMLR 2026 Kernel-Based Safe Exploration in Deep Reinforcement Learning L4DC 2026 Kernel Integrated R^2: A Measure of Dependence UAI 2026 Kernel Mean Embedding Deviation Subspace for Unsupervised Learning with Heterogeneous Data JMLR 2026 KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching UAI 2026 Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation JMLR 2026 Koopman Operator for Stability Analysis: Theory with a Linear–Radial Product Reproducing Kernel L4DC 2026 Learning Expressive Random Feature Models via Parametrized Activations UAI 2026 Margin in Abstract Spaces COLT 2026 Nonlinear function-on-function regression by RKHS JMLR 2026 NTK-Guided Implicit Neural Teaching CVPR 2026 Spectral Truncation Kernels: Noncommutativity in C*-algebraic Kernel Machines JMLR 2026 SteinGate: Tail-Sensitive Safe Reinforcement Learning via Stein Discrepancy UAI 2026 The Generalised Kernel Covariance Measure CLeaR 2026 Uncertainty Quantification for Regression: A Unified Framework based on kernel scores UAI 2026