243 citations · 243 across the 3 of their papers we have counts for
4 papers
Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations
Tong Zhang, Jiangning Zhang, Zhucun Xue +9
Balancing convergence speed, generalization capability, and computational efficiency remains a core challenge in deep learning optimization. First-order gradient descent methods, e…
On the Benefits of Over-parameterization for Out-of-Distribution Generalization
Yifan Hao, Yong Lin, Difan Zou +1
In recent years, machine learning models have achieved success based on the independently and identically distributed assumption. However, this assumption can be easily violated in…
The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness
Yifan Hao, Tong Zhang
Recent empirical and theoretical studies have established the generalization capabilities of large machine learning models that are trained to (approximately or exactly) fit noisy…
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
Shai Shalev-Shwartz, Tong Zhang
Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarante…