1 citations · 1 across the 6 of their papers we have counts for
7 papers
Adam under Generalized Smoothness with Second-Moment-Type Stochastic Gradients
Ruinan Jin, Difei Cheng, Ling Chen +3
Adam is widely observed to remain stable even when the objective deviates significantly from global smoothness. Under the generalized smoothness framework, however, existing analys…
A Decomposition Framework for Certifiably Optimal Orthogonal Sparse PCA
Difei Cheng, Qiao Hu
Sparse Principal Component Analysis (SPCA) is an important technique for high-dimensional data analysis, improving interpretability by imposing sparsity on principal components. Ho…
Online AUC Optimization Based on Second-order Surrogate Loss
JunRu Luo, Difei Cheng, Bo Zhang
The Area Under the Curve (AUC) is an important performance metric for classification tasks, particularly in class-imbalanced scenarios. However, minimizing the AUC presents signifi…
Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum
Difei Cheng, Ruinan Jin, Hong Qiao +1
Distributed stochastic gradient methods are widely used to preserve data privacy and ensure scalability in large-scale learning tasks. While existing theory on distributed momentum…
Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods
Ruinan Jin, Difei Cheng, Hong Qiao +3
Stochastic Gradient Descent (SGD) is widely used in machine learning research. Previous convergence analyses of SGD under the vanishing step-size setting typically require Robbins-…
Careful Seeding for k-Medois Clustering with Incremental k-Means++ Initialization
Difei Cheng, Yunfeng Zhang, Ruinan Jin
K-medoids clustering is a popular variant of k-means clustering and widely used in pattern recognition and machine learning. A main drawback of k-medoids clustering is that an impr…
Fast Density Estimation for Density-based Clustering Methods
Difei Cheng, Ruihang Xu, Bo Zhang +1
Density-based clustering algorithms are widely used for discovering clusters in pattern recognition and machine learning since they can deal with non-hyperspherical clusters and ar…