kernel methods

Kernelized Diffusion Maps COLT 2023 Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network ICML 2023 Kernel Neural Optimal Transport ICLR 2023 Kernel Quadrature with Randomly Pivoted Cholesky NeurIPS 2023 Kernel QuantTree ICML 2023 Kernel Sufficient Dimension Reduction and Variable Selection for Compositional Data via Amalgamation ICML 2023 K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing NeurIPS 2023 Leaky Hockey Stick Loss: The First Negatively Divergent Margin-based Loss Function for Classification JMLR 2023 Learning in RKHM: a C*-Algebraic Twist for Kernel Machines AISTATS 2023 Lifelong bandit optimization: no prior and no regret UAI 2023 LinK: Linear Kernel for LiDAR-Based 3D Perception CVPR 2023 Metrizing Weak Convergence with Maximum Mean Discrepancies JMLR 2023 Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension NeurIPS 2023 Minimax Risk Classifiers with 0-1 Loss JMLR 2023 MMD Aggregated Two-Sample Test JMLR 2023 MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting NeurIPS 2023 Nearly-tight Bounds for Deep Kernel Learning ICML 2023 Nonparametric Gaussian Process Covariances via Multidimensional Convolutions AISTATS 2023 Nyström M-Hilbert-Schmidt independence criterion UAI 2023 On Distance and Kernel Measures of Conditional Dependence JMLR 2023 On kernel-based statistical learning theory in the mean field limit NeurIPS 2023 On the Identifiability and Interpretability of Gaussian Process Models NeurIPS 2023 Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference ICML 2023 Partial Label Learning with Dissimilarity Propagation guided Candidate Label Shrinkage NeurIPS 2023 Perceptronic Complexity and Online Matrix Completion ALT 2023