1 code implementation • 11 Jul 2020 • Yimin Huang, Yu-Jun Li, Hanrong Ye, Zhenguo Li, Zhihua Zhang
The evaluation of hyperparameters, neural architectures, or data augmentation policies becomes a critical model selection problem in advanced deep learning with a large hyperparameter search space.
no code implementations • 13 Sep 2019 • Luo Luo, Cheng Chen, Yu-Jun Li, Guangzeng Xie, Zhihua Zhang
We consider saddle point problems which objective functions are the average of $n$ strongly convex-concave individual components.
no code implementations • 27 Oct 2017 • Jean Honorio, Yu-Jun Li
We show that the error probability of reconstructing kernel matrices from Random Fourier Features for the Gaussian kernel function is at most $\mathcal{O}(R^{2/3} \exp(-D))$, where $D$ is the number of random features and $R$ is the diameter of the data domain.