47 citations · 214 across the 48 of their papers we have counts for
8 papers · 1 filter
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
Jiyan He, Xuechen Li, Da Yu +6
Differentially private deep learning has recently witnessed advances in computational efficiency and privacy-utility trade-off. We explore whether further improvements along the tw…
Similarity Distribution based Membership Inference Attack on Person Re-identification
Junyao Gao, Xinyang Jiang, Huishuai Zhang +6
While person Re-identification (Re-ID) has progressed rapidly due to its wide real-world applications, it also causes severe risks of leaking personal information from training dat…
Denoising Masked AutoEncoders Help Robust Classification
Quanlin Wu, Hang Ye, Yuntian Gu +3
In this paper, we propose a new self-supervised method, which is called Denoising Masked AutoEncoders (DMAE), for learning certified robust classifiers of images. In DMAE, we corru…
Provable Adaptivity of Adam under Non-uniform Smoothness
Bohan Wang, Yushun Zhang, Huishuai Zhang +6
Adam is widely adopted in practical applications due to its fast convergence. However, its theoretical analysis is still far from satisfactory. Existing convergence analyses for Ad…
Normalized/Clipped SGD with Perturbation for Differentially Private Non-Convex Optimization
Xiaodong Yang, Huishuai Zhang, Wei Chen +1
By ensuring differential privacy in the learning algorithms, one can rigorously mitigate the risk of large models memorizing sensitive training data. In this paper, we study two al…
Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks
Huishuai Zhang, Da Yu, Yiping Lu +1
Adversarial examples, which are usually generated for specific inputs with a specific model, are ubiquitous for neural networks. In this paper we unveil a surprising property of ad…