37 citations · 97 across the 26 of their papers we have counts for
6 papers · 1 filter
Weighted Distributed Differential Privacy ERM: Convex and Non-convex
Yilin Kang, Yong Liu, Weiping Wang
Distributed machine learning is an approach allowing different parties to learn a model over all data sets without disclosing their own data. In this paper, we propose a weighted d…
Automated Spectral Kernel Learning
Jian Li, Yong Liu, Weiping Wang
The generalization performance of kernel methods is largely determined by the kernel, but common kernels are stationary thus input-independent and output-independent, that limits t…
Semi-supervised Vector-valued Learning: Improved Bounds and Algorithms
Jian Li, Yong Liu, Weiping Wang
Vector-valued learning, where the output space admits a vector-valued structure, is an important problem that covers a broad family of important domains, e.g. multi-task learning a…
Towards Sharp Analysis for Distributed Learning with Random Features
Jian Li, Yong Liu, Weiping Wang
In recent studies, the generalization properties for distributed learning and random features assumed the existence of the target concept over the hypothesis space. However, this s…
Efficient Cross-Validation for Semi-Supervised Learning
Yong Liu, Jian Li, Guangjun Wu +2
Manifold regularization, such as laplacian regularized least squares (LapRLS) and laplacian support vector machine (LapSVM), has been widely used in semi-supervised learning, and i…
Max-Diversity Distributed Learning: Theory and Algorithms
Yong Liu, Jian Li, Weiping Wang
We study the risk performance of distributed learning for the regularization empirical risk minimization with fast convergence rate, substantially improving the error analysis of t…