17 citations · 24 across the 4 of their papers we have counts for
5 papers
Pareto-Improving Adversarial Attacks with Primal-Dual Regularization
Yang Dai, Longfei Zhang, Wei Tao +3
Transferable adversarial attacks are arguably the most practical black-box threat model. Under the same perturbation budget, stronger transfer attacks attain higher attack success…
Adapting Step-size: A Unified Perspective to Analyze and Improve Gradient-based Methods for Adversarial Attacks
Wei Tao, Lei Bao, Sheng Long +2
Learning adversarial examples can be formulated as an optimization problem of maximizing the loss function with some box-constraints. However, for solving this induced optimization…
The Role of Momentum Parameters in the Optimal Convergence of Adaptive Polyak's Heavy-ball Methods
Wei Tao, Sheng Long, Gaowei Wu +1
The adaptive stochastic gradient descent (SGD) with momentum has been widely adopted in deep learning as well as convex optimization. In practice, the last iterate is commonly used…
Gradient Descent Averaging and Primal-dual Averaging for Strongly Convex Optimization
Wei Tao, Wei Li, Zhisong Pan +1
Averaging scheme has attracted extensive attention in deep learning as well as traditional machine learning. It achieves theoretically optimal convergence and also improves the emp…
The Strength of Nesterov's Extrapolation in the Individual Convergence of Nonsmooth Optimization
W. Tao, Z. Pan, G. Wu +1
The extrapolation strategy raised by Nesterov, which can accelerate the convergence rate of gradient descent methods by orders of magnitude when dealing with smooth convex objectiv…