2 citations · 2 across the 6 of their papers we have counts for
6 papers
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-…
Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks
Xin-Chun Li, Jin-Lin Tang, Bo Zhang +2
Exploring the loss landscape offers insights into the inherent principles of deep neural networks (DNNs). Recent work suggests an additional asymmetry of the valley beyond the flat…
Asymptotic Inference for Infinitely Imbalanced Logistic Regression
Dorian Goldman, Bo Zhang
In this paper we extend the work of Owen (2007) by deriving a second order expansion for the slope parameter in logistic regression, when the size of the majority class is unbounde…
Realization of spatial sparseness by deep ReLU nets with massive data
Charles K. Chui, Shao-Bo Lin, Bo Zhang +1
The great success of deep learning poses urgent challenges for understanding its working mechanism and rationality. The depth, structure, and massive size of the data are recognize…