Kernel Methods

Graph-Aided Online Multi-Kernel Learning JMLR 2023 Improved Kernel Alignment Regret Bound for Online Kernel Learning AAAI 2023 Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes NeurIPS 2023 Isotropic Gaussian Processes on Finite Spaces of Graphs AISTATS 2023 Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches JMLR 2023 Kernelized Diffusion Maps COLT 2023 Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network ICML 2023 Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition JMLR 2023 Kernel QuantTree ICML 2023 Learning Kernelized Contextual Bandits in a Distributed and Asynchronous Environment ICLR 2023 Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces JMLR 2023 Minimax Optimal Kernel Operator Learning via Multilevel Training ICLR 2023 Minimax Risk Classifiers with 0-1 Loss JMLR 2023 Nearly Optimal Algorithms with Sublinear Computational Complexity for Online Kernel Regression ICML 2023 Nearly-tight Bounds for Deep Kernel Learning ICML 2023 On The Relative Error of Random Fourier Features for Preserving Kernel Distance ICLR 2023 On the Saturation Effect of Kernel Ridge Regression ICLR 2023 Optimal Convergence Rates for Agnostic Nyström Kernel Learning ICML 2023 Optimal Convergence Rates for Distributed Nystroem Approximation JMLR 2023 Overparameterized Random Feature Regression with Nearly Orthogonal Data AISTATS 2023 Practical Markov Boundary Learning without Strong Assumptions AAAI 2023 Robustifying Generalizable Implicit Shape Networks with a Tunable Non-Parametric Model NeurIPS 2023 Sample based Explanations via Generalized Representers NeurIPS 2023 Sample Complexity of Kernel-Based Q-Learning AISTATS 2023 Scalable Unbalanced Sobolev Transport for Measures on a Graph AISTATS 2023