3 papers
cs.LG2023
Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels
Fan He, Mingzhen He, Lei Shi +2
The lack of sufficient flexibility is the key bottleneck of kernel-based learning that relies on manually designed, pre-given, and non-trainable kernels. To enhance kernel flexibil…
cs.LG2022
Random Fourier Features for Asymmetric Kernels
Mingzhen He, Fan He, Fanghui Liu +1
The random Fourier features (RFFs) method is a powerful and popular technique in kernel approximation for scalability of kernel methods. The theoretical foundation of RFFs is based…
cs.LG2022
Learning with Asymmetric Kernels: Least Squares and Feature Interpretation
Mingzhen He, Fan He, Lei Shi +2
Asymmetric kernels naturally exist in real life, e.g., for conditional probability and directed graphs. However, most of the existing kernel-based learning methods require kernels…