2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning
Fan He, Mingzhen He, Lei Shi +2
Ridgeless regression has garnered attention among researchers, particularly in light of the ``Benign Overfitting'' phenomenon, where models interpolating noisy samples demonstrate…
cs.LG2024★ 2 cited
Revisiting Random Weight Perturbation for Efficiently Improving Generalization
Tao Li, Qinghua Tao, Weihao Yan +5
Improving the generalization ability of modern deep neural networks (DNNs) is a fundamental challenge in machine learning. Two branches of methods have been proposed to seek flat m…
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…