2 citations · 4 across the 18 of their papers we have counts for
6 papers · 1 filter
Large Dimensional Kernel Ridge Regression: Extending to Product Kernels
Yang Zhou, Yicheng Li, Yuqian Cheng +1
Recent studies have reported and in large dimensional kernel ridge regression (KRR). However, these findings are…
Learning Curves and Benign Overfitting of Spectral Algorithms in Large Dimensions
Weihao Lu, Qian Lin, Yingcun Xia +1
Existing large-dimensional theory for spectral algorithms resolves either the optimally tuned point or the interpolation limit, but leaves the under-regularized regime unexplored.…
On the Saturation Effects of Spectral Algorithms in Large Dimensions
Weihao Lu, Haobo Zhang, Yicheng Li +1
The saturation effects, which originally refer to the fact that kernel ridge regression (KRR) fails to achieve the information-theoretical lower bound when the regression function…
On the Impacts of the Random Initialization in the Neural Tangent Kernel Theory
Guhan Chen, Yicheng Li, Qian Lin
This paper aims to discuss the impact of random initialization of neural networks in the neural tangent kernel (NTK) theory, which is ignored by most recent works in the NTK theory…
On the Saturation Effect of Kernel Ridge Regression
Yicheng Li, Haobo Zhang, Qian Lin
The saturation effect refers to the phenomenon that the kernel ridge regression (KRR) fails to achieve the information theoretical lower bound when the smoothness of the undergroun…
Optimal Rate of Kernel Regression in Large Dimensions
Weihao Lu, Haobo Zhang, Yicheng Li +2
We perform a study on kernel regression for large-dimensional data (where the sample size is polynomially depending on the dimension of the samples, i.e., for…