2 citations · 3 across the 8 of their papers we have counts for
8 papers
Nora: Normalized Orthogonal Row Alignment for Scalable Matrix Optimizer
Jinghui Yuan, Jiaxuan Zou, Shuo Wang +2
Matrix-based optimizers have demonstrated immense potential in training Large Language Models (LLMs), however, designing an ideal optimizer remains a formidable challenge. A superi…
Cooperation of Experts: Fusing Heterogeneous Information with Large Margin
Shuo Wang, Shunyang Huang, Jinghui Yuan +2
Fusing heterogeneous information remains a persistent challenge in modern data analysis. While significant progress has been made, existing approaches often fail to account for the…
Riemannian Optimization on Relaxed Indicator Matrix Manifold
Jinghui Yuan, Fangyuan Xie, Feiping Nie +1
The indicator matrix plays an important role in machine learning, but optimizing it is an NP-hard problem. We propose a new relaxation of the indicator matrix and prove that this r…
Double-Bounded Nonlinear Optimal Transport for Size Constrained Min Cut Clusterin
Fangyuan Xie, Jinghui Yuan, Feiping Nie +1
Min cut is an important graph partitioning method. However, current solutions to the min cut problem suffer from slow speeds, difficulty in solving, and often converge to simple so…
A Margin-Maximizing Fine-Grained Ensemble Method
Jinghui Yuan, Hao Chen, Renwei Luo +1
Ensemble learning has achieved remarkable success in machine learning, but its reliance on numerous base learners limits its application in resource-constrained environments. This…
Multi-Task Curriculum Graph Contrastive Learning with Clustering Entropy Guidance
Chusheng Zeng, Bocheng Wang, Jinghui Yuan +2
Recent advances in unsupervised deep graph clustering have been significantly promoted by contrastive learning. Despite the strides, most graph contrastive learning models face cha…
Achieving More with Less: A Tensor-Optimization-Powered Ensemble Method
Jinghui Yuan, Weijin Jiang, Zhe Cao +4
Ensemble learning is a method that leverages weak learners to produce a strong learner. However, obtaining a large number of base learners requires substantial time and computation…
Doubly Stochastic Adaptive Neighbors Clustering via the Marcus Mapping
Jinghui Yuan, Chusheng Zeng, Fangyuan Xie +5
Clustering is a fundamental task in machine learning and data science, and similarity graph-based clustering is an important approach within this domain. Doubly stochastic symmetri…