L
Li Zhang
Researcher at Google
Publications - 158
Citations - 13353
Li Zhang is an academic researcher from Google. The author has contributed to research in topics: Computer science & Differential privacy. The author has an hindex of 44, co-authored 136 publications receiving 9699 citations. Previous affiliations of Li Zhang include Microsoft & Stony Brook University.
Papers
More filters
Proceedings ArticleDOI
Deep Learning with Differential Privacy
TL;DR: In this paper, the authors develop new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy, and demonstrate that they can train deep neural networks with nonconvex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.
Proceedings ArticleDOI
Deep Learning with Differential Privacy
TL;DR: This work develops new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy, and demonstrates that deep neural networks can be trained with non-convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.
Proceedings ArticleDOI
Scalable Influence Maximization in Social Networks under the Linear Threshold Model
Wei Chen,Yifei Yuan,Li Zhang +2 more
TL;DR: This paper proposes the first scalable influence maximization algorithm tailored for the linear threshold model, which is scalable to networks with millions of nodes and edges, is orders of magnitude faster than the greedy approximation algorithm proposed by Kempe et al. and its optimized versions, and performs consistently among the best algorithms.
Posted Content
Learning Differentially Private Recurrent Language Models
TL;DR: This work builds on recent advances in the training of deep networks on user-partitioned data and privacy accounting for stochastic gradient descent and adds user-level privacy protection to the federated averaging algorithm, which makes "large step" updates from user- level data.
Proceedings ArticleDOI
Geometric spanner for routing in mobile networks
TL;DR: It is shown by simulation that the RDG outperforms previously proposed routing graphs in the context of the Greedy perimeter stateless routing (GPSR) protocol, and theoretical bounds on the quality of paths discovered using GPSR are investigated.