1.8k citations · 2k across the 18 of their papers we have counts for
38 papers
Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor
Lijun Zhang, Wei Jiang, Jinfeng Yi +1
In this paper, we investigate an online prediction strategy named as Discounted-Normal-Predictor (Kapralov and Panigrahy, 2010) for smoothed online convex optimization (SOCO), in w…
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
Yimeng Zhang, Yuguang Yao, Jinghan Jia +4
The lack of adversarial robustness has been recognized as an important issue for state-of-the-art machine learning (ML) models, e.g., deep neural networks (DNNs). Thereby, robustif…
How and When Adversarial Robustness Transfers in Knowledge Distillation?
Rulin Shao, Jinfeng Yi, Pin-Yu Chen +1
Knowledge distillation (KD) has been widely used in teacher-student training, with applications to model compression in resource-constrained deep learning. Current works mainly foc…
Training Meta-Surrogate Model for Transferable Adversarial Attack
Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi +1
We consider adversarial attacks to a black-box model when no queries are allowed. In this setting, many methods directly attack surrogate models and transfer the obtained adversari…
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy
Xinwei Zhang, Xiangyi Chen, Mingyi Hong +2
Providing privacy protection has been one of the primary motivations of Federated Learning (FL). Recently, there has been a line of work on incorporating the formal privacy notion…
Towards Heterogeneous Clients with Elastic Federated Learning
Zichen Ma, Yu Lu, Zihan Lu +3
Federated learning involves training machine learning models over devices or data silos, such as edge processors or data warehouses, while keeping the data local. Training in heter…