368 citations · 1.4k across the 51 of their papers we have counts for
51 papers
First-Order Methods for Solving Convex (Strongly) Concave Minimax Problems with Functional Constraints
Yangyang Xu, Xinhao Ying
Minimax problems arise in many applications, including robust learning and Stackelberg models. Most existing methods for minimax problems address unconstrained or projection-friend…
Alternating Direction Method of Multipliers for nonlinear constrained convex problems and applications to distributed resource allocation and constrained machine learning
Zhengjie Xiong, Yangyang Xu
We study a class of structured convex optimization problems, which have a two-block separable objective and nonlinear functional constraints as well as affine constraints that coup…
A variance reduced framework for (non)smooth nonconvex-nonconcave stochastic minimax problems with extended Kurdyka-Lojasiewicz property
Muhammad Khan, Yangyang Xu
In this paper, we study stochastic constrained minimax optimization problems with nonconvex-nonconcave structure, a central problem in modern machine learning, for which reliable a…
A Depth-Independent Linear Chain Ansatz for Large-Scale Quantum Approximate Optimization
Zixu Wang, Jack Mandell, Yangyang Xu +1
Combinatorial optimization lies at the heart of numerous real-world applications. For a broad category of optimization problems, quantum computing is expected to exhibit quantum sp…
Neighbor-Sampling Based Momentum Stochastic Methods for Training Graph Neural Networks
Molly Noel, Gabriel Mancino-Ball, Yangyang Xu
Graph convolutional networks (GCNs) are a powerful tool for graph representation learning. Due to the recursive neighborhood aggregations employed by GCNs, efficient training metho…
TimeCapsule: Solving the Jigsaw Puzzle of Long-Term Time Series Forecasting with Compressed Predictive Representations
Yihang Lu, Yangyang Xu, Qitao Qing +1
Recent deep learning models for Long-term Time Series Forecasting (LTSF) often emphasize complex, handcrafted designs, while simpler architectures like linear models or MLPs have o…