Kernel Methods

The Inductive Bias of Quantum Kernels NeurIPS 2021 A Cluster-Weighted Kernel K-Means Method for Multi-View Clustering AAAI 2020 A kernel test for quasi-independence NeurIPS 2020 Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity COLT 2020 A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent NeurIPS 2020 Automated Spectral Kernel Learning AAAI 2020 Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach L4DC 2020 Conjugate Gradients for Kernel Machines JMLR 2020 Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections JMLR 2020 Distributed Kernel Ridge Regression with Communications JMLR 2020 Finite Versus Infinite Neural Networks: an Empirical Study NeurIPS 2020 Fourier Sparse Leverage Scores and Approximate Kernel Learning NeurIPS 2020 Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling AISTATS 2020 Hard Shape-Constrained Kernel Machines NeurIPS 2020 Hypothesis Sketching for Online Kernel Selection in Continuous Kernel Space IJCAI 2020 Kernel Alignment Risk Estimator: Risk Prediction from Training Data NeurIPS 2020 Kernel-based Approach for Learning Causal Graphs from Mixed Data PGM 2020 Kernelized Wasserstein Natural Gradient ICLR 2020 Kernel Methods for Cooperative Multi-Agent Contextual Bandits ICML 2020 Kernel Methods Through the Roof: Handling Billions of Points Efficiently NeurIPS 2020 Kernel Taylor-Based Value Function Approximation for Continuous-State Markov Decision Processes RSS 2020 Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity NeurIPS 2020 Learning Data-adaptive Non-parametric Kernels JMLR 2020 Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces NeurIPS 2020 Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions NeurIPS 2020