8 papers
FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization
Zhiyuan Ning, Chunlin Tian, Meng Xiao +5
Federated Learning faces significant challenges in statistical and system heterogeneity, along with high energy consumption, necessitating efficient client selection strategies. Tr…
scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph Embedding
Ping Xu, Zhiyuan Ning, Meng Xiao +4
Single-cell RNA sequencing (scRNA-seq) is essential for unraveling cellular heterogeneity and diversity, offering invaluable insights for bioinformatics advancements. Despite its p…
Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning
Weiliang Zhang, Xiaohan Huang, Yi Du +5
Feature selection aims to preprocess the target dataset, find an optimal and most streamlined feature subset, and enhance the downstream machine learning task. Among filter, wrappe…
Knowledge-Guided Biomarker Identification for Label-Free Single-Cell RNA-Seq Data: A Reinforcement Learning Perspective
Meng Xiao, Weiliang Zhang, Xiaohan Huang +4
Gene panel selection aims to identify the most informative genomic biomarkers in label-free genomic datasets. Traditional approaches, which rely on domain expertise, embedded machi…
Soft Graph Clustering for single-cell RNA Sequencing Data
Ping Xu, Pengfei Wang, Zhiyuan Ning +3
Clustering analysis is fundamental in single-cell RNA sequencing (scRNA-seq) data analysis for elucidating cellular heterogeneity and diversity. Recent graph-based scRNA-seq cluste…
Reinforcement Learning-based Feature Generation Algorithm for Scientific Data
Meng Xiao, Junfeng Zhou, Yuanchun Zhou
Feature generation (FG) aims to enhance the prediction potential of original data by constructing high-order feature combinations and removing redundant features. It is a key prepr…