22 citations · 23 across the 2 of their papers we have counts for
4 papers · 1 filter
Learning to Generalize Provably in Learning to Optimize
Junjie Yang, Tianlong Chen, Mingkang Zhu +4
Learning to optimize (L2O) has gained increasing popularity, which automates the design of optimizers by data-driven approaches. However, current L2O methods often suffer from poor…
Bilevel Optimization: Convergence Analysis and Enhanced Design
Kaiyi Ji, Junjie Yang, Yingbin Liang
Bilevel optimization has arisen as a powerful tool for many machine learning problems such as meta-learning, hyperparameter optimization, and reinforcement learning. In this paper,…
Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning
Kaiyi Ji, Junjie Yang, Yingbin Liang
As a popular meta-learning approach, the model-agnostic meta-learning (MAML) algorithm has been widely used due to its simplicity and effectiveness. However, the convergence of the…
SGD Converges to Global Minimum in Deep Learning via Star-convex Path
Yi Zhou, Junjie Yang, Huishuai Zhang +2
Stochastic gradient descent (SGD) has been found to be surprisingly effective in training a variety of deep neural networks. However, there is still a lack of understanding on how…