10 citations · 11 across the 4 of their papers we have counts for
6 papers · 1 filter
Sharper Utility Bounds for Differentially Private Models
Yilin Kang, Yong Liu, Jian Li +1
In this paper, by introducing Generalized Bernstein condition, we propose the first high probability excess population risk bound for dif…
Stability and Generalization of Differentially Private Minimax Problems
Yilin Kang, Yong Liu, Jian Li +1
In the field of machine learning, many problems can be formulated as the minimax problem, including reinforcement learning, generative adversarial networks, to just name a few. So…
Towards Sharper Utility Bounds for Differentially Private Pairwise Learning
Yilin Kang, Yong Liu, Jian Li +1
Pairwise learning focuses on learning tasks with pairwise loss functions, depends on pairs of training instances, and naturally fits for modeling relationships between pairs of sam…
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…
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…