← Bayesian & Probabilistic
Machine Learning › Bayesian & Probabilistic ›

Kernel Methods

96 directly classified papers

Papers per year

Papers

Randomized Sketches for Clustering: Fast and Optimal Kernel $k$-Means NIPS 2022
Large-Scale Learning with Fourier Features and Tensor Decompositions NIPS 2021
Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes NIPS 2021
Isolation Graph Kernel AAAI 2021
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction ICML 2021
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression ICML 2021
Hierarchical Multiple Kernel Clustering AAAI 2021
Effective Meta-Regularization by Kernelized Proximal Regularization NIPS 2021
Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation NIPS 2021
Structure-Aware Random Fourier Kernel for Graphs NIPS 2021
Automated Spectral Kernel Learning AAAI 2020
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms JMLR 2020
Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces NIPS 2020
Kernel Alignment Risk Estimator: Risk Prediction from Training Data NIPS 2020
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning NIPS 2020
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration NIPS 2019
Approximate Kernel Selection with Strong Approximate Consistency AAAI 2019
Distributionally Robust Optimization and Generalization in Kernel Methods NIPS 2019
Copula Multi-label Learning NIPS 2019
Kernelized Hashcode Representations for Relation Extraction AAAI 2019
Zero-Shot Kernel Learning CVPR 2018
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions EMNLP 2018
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams NIPS 2018
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces NIPS 2018
Multi-Class Learning: From Theory to Algorithm NIPS 2018