37 citations · 97 across the 26 of their papers we have counts for
6 papers · 1 filter
Neural Architecture Optimization with Graph VAE
Jian Li, Yong Liu, Jiankun Liu +1
Due to their high computational efficiency on a continuous space, gradient optimization methods have shown great potential in the neural architecture search (NAS) domain. The mappi…
Theoretical Analysis of Divide-and-Conquer ERM: Beyond Square Loss and RKHS
Yong Liu, Lizhong Ding, Weiping Wang
Theoretical analysis of the divide-and-conquer based distributed learning with least square loss in the reproducing kernel Hilbert space (RKHS) have recently been explored within t…
Nearly Optimal Clustering Risk Bounds for Kernel K-Means
Yong Liu, Lizhong Ding, Weiping Wang
In this paper, we study the statistical properties of kernel -means and obtain a nearly optimal excess clustering risk bound, substantially improving the state-of-art bounds in…
Convolutional Spectral Kernel Learning
Jian Li, Yong Liu, Weiping Wang
Recently, non-stationary spectral kernels have drawn much attention, owing to its powerful feature representation ability in revealing long-range correlations and input-dependent c…
Input Perturbation: A New Paradigm between Central and Local Differential Privacy
Yilin Kang, Yong Liu, Ben Niu +3
Traditionally, there are two models on differential privacy: the central model and the local model. The central model focuses on the machine learning model and the local model focu…
Data Heterogeneity Differential Privacy: From Theory to Algorithm
Yilin Kang, Jian Li, Yong Liu +1
Traditionally, the random noise is equally injected when training with different data instances in the field of differential privacy (DP). In this paper, we first give sharper exce…