36 citations · 141 across the 18 of their papers we have counts for
6 papers · 1 filter
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization
Qingsong Zhang, Bin Gu, Cheng Deng +4
Vertical federated learning (VFL) is an effective paradigm of training the emerging cross-organizational (e.g., different corporations, companies and organizations) collaborative l…
An Accelerated Variance-Reduced Conditional Gradient Sliding Algorithm for First-order and Zeroth-order Optimization
Xiyuan Wei, Bin Gu, Heng Huang
The conditional gradient algorithm (also known as the Frank-Wolfe algorithm) has recently regained popularity in the machine learning community due to its projection-free property…
Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients
Huimin Wu, Zhengmian Hu, Bin Gu
Adversarial attacks by generating examples which are almost indistinguishable from natural examples, pose a serious threat to learning models. Defending against adversarial attacks…
Learning Sampling Policy for Faster Derivative Free Optimization
Zhou Zhai, Bin Gu, Heng Huang
Zeroth-order (ZO, also known as derivative-free) methods, which estimate the gradient only by two function evaluations, have attracted much attention recently because of its broad…
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating
Qingsong Zhang, Bin Gu, Cheng Deng +1
Vertical federated learning (VFL) attracts increasing attention due to the emerging demands of multi-party collaborative modeling and concerns of privacy leakage. In the real VFL a…
Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm
Bin Gu, Guodong Liu, Yanfu Zhang +2
Modern machine learning algorithms usually involve tuning multiple (from one to thousands) hyperparameters which play a pivotal role in terms of model generalizability. Black-box o…