51 citations · 106 across the 32 of their papers we have counts for
6 papers · 1 filter
Stochastic Inexact Augmented Lagrangian Method for Nonconvex Expectation Constrained Optimization
Zichong Li, Pin-Yu Chen, Sijia Liu +2
Many real-world problems not only have complicated nonconvex functional constraints but also use a large number of data points. This motivates the design of efficient stochastic me…
ASGNN: Graph Neural Networks with Adaptive Structure
Zepeng Zhang, Songtao Lu, Zengfeng Huang +1
The graph neural network (GNN) models have presented impressive achievements in numerous machine learning tasks. However, many existing GNN models are shown to be vulnerable to adv…
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks
Zhuqing Liu, Xin Zhang, Prashant Khanduri +2
In recent years, decentralized bilevel optimization problems have received increasing attention in the networking and machine learning communities thanks to their versatility in mo…
Understanding Benign Overfitting in Gradient-Based Meta Learning
Lisha Chen, Songtao Lu, Tianyi Chen
Meta learning has demonstrated tremendous success in few-shot learning with limited supervised data. In those settings, the meta model is usually overparameterized. While the conve…
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
Gaoyuan Zhang, Songtao Lu, Yihua Zhang +7
Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against…
Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning
Alex Gu, Songtao Lu, Parikshit Ram +1
We consider a generic min-max multi-objective bilevel optimization problem with applications in robust machine learning such as representation learning and hyperparameter optimizat…