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Kernel Methods

571 directly classified papers

Papers per year

Papers

Skill Disentanglement in Reproducing Kernel Hilbert Space AAAI 2025
The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs JMLR 2025
Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection JMLR 2025
DeMo: Deep Motion Field Consensus with Learnable Kernels for Two-view Correspondence Learning AAAI 2025
Unsupervised Kernel-based Multi-view Feature Selection with Robust Self-representation and Binary Hashing AAAI 2025
Fast Second-Order Online Kernel Learning Through Incremental Matrix Sketching and Decomposition IJCAI 2025
Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling JMLR 2025
Biological Sequence Kernels with Guaranteed Flexibility JMLR 2025
Kernel-based L_2-Boosting with Structure Constraints JMLR 2025
Toward Interpretable Time Series Modeling: A Kernel Representation Perspective IJCAI 2025
KOALA: Kernel Coupling and Element Imputation Induced Multi-View Clustering AAAI 2025
Bridging the Gap Between Hyperdimensional Computing and Kernel Methods via the Nyström Method AAAI 2025
Incremental Nyström-based Multiple Kernel Clustering AAAI 2025
DPO Kernels: A Semantically-Aware, Kernel-Enhanced, and Divergence-Rich Paradigm for Direct Preference Optimization ACL 2025
On the Approximation of Kernel functions JMLR 2025
Variance-Aware Estimation of Kernel Mean Embedding JMLR 2025
Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos JMLR 2025
Statistical inference on black-box generative models in the data kernel perspective space ACL 2025
Optimal Functional Bilinear Regression with Two-dimensional Functional Covariates via Reproducing Kernel Hilbert Space JMLR 2025
Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm NIPS 2024
Distributed Kernel-Driven Data Clustering JMLR 2024
Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces JMLR 2024
Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regression AISTATS 2024
A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression NIPS 2024
Large-Scale Gaussian Processes via Alternating Projection AISTATS 2024