activity
20222026
most citedWelfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

A Systematic Evaluation of Imbalance Handling Methods in Biomedical Binary Classification

Jiandong Chen, Lingjie Su, Le Peng +3

Objective: The primary goal of this study was to systematically examine the impact of commonly used imbalance handling methods (IHMs) on predictive performance in biomedical binary…

physics.optics2025

Circulating tumor cell detection in cancer patients using in-flow deep learning holography

Kevin Mallery, Nathaniel R. Bristow, Nicholas Heller +20

Circulating tumor cells (CTCs) are cancer cells found in the bloodstream that serve as biomarkers for early cancer detection, prognostication, and disease monitoring. However, CTC…

cs.LG2025

Exact Reformulation and Optimization for Direct Metric Optimization in Binary Imbalanced Classification

Le Peng, Yash Travadi, Chuan He +2

For classification with imbalanced class frequencies, i.e., imbalanced classification (IC), standard accuracy is known to be misleading as a performance measure. While most existin…

cs.LG2023★ 2 cited

Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective

Yash Travadi, Le Peng, Xuan Bi +2

Federated learning (FL) is a privacy-preserving learning technique that enables distributed computing devices to train shared learning models across data silos collaboratively. Exi…

cs.CV2022★ 1 cited

Imbalanced Classification in Medical Imaging via Regrouping

Le Peng, Yash Travadi, Rui Zhang +2

We propose performing imbalanced classification by regrouping majority classes into small classes so that we turn the problem into balanced multiclass classification. This new idea…