118 citations · 236 across the 20 of their papers we have counts for
20 papers
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
Tianrun Yu, Jiaqi Wang, Haoyu Wang +4
Collaborative fairness is a crucial challenge in federated learning. However, existing approaches often overlook a practical yet complex form of heterogeneity: imbalanced covariate…
BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models
Aofei Chang, Jiaqi Wang, Han Liu +4
Parameter Efficient Fine-Tuning (PEFT) offers an efficient solution for fine-tuning large pretrained language models for downstream tasks. However, most PEFT strategies are manuall…
FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection
Jiaqi Wang, Xiaochen Wang, Lingjuan Lyu +2
This study introduces the Federated Medical Knowledge Injection (FEDMEKI) platform, a new benchmark designed to address the unique challenges of integrating medical knowledge into…
FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models
Xiaochen Wang, Jiaqi Wang, Houping Xiao +2
Foundation models have demonstrated remarkable capabilities in handling diverse modalities and tasks, outperforming conventional artificial intelligence (AI) approaches that are hi…
Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models
Yuan Zhong, Xiaochen Wang, Jiaqi Wang +5
Synthesizing electronic health records (EHR) data has become a preferred strategy to address data scarcity, improve data quality, and model fairness in healthcare. However, existin…
Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality
Liwei Che, Jiaqi Wang, Xinyue Liu +1
Federated learning (FL) has obtained tremendous progress in providing collaborative training solutions for distributed data silos with privacy guarantees. However, few existing wor…
CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning
Junyu Luo, Xiaochen Wang, Jiaqi Wang +3
Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing.…
Recent Advances in Predictive Modeling with Electronic Health Records
Jiaqi Wang, Junyu Luo, Muchao Ye +8
The development of electronic health records (EHR) systems has enabled the collection of a vast amount of digitized patient data. However, utilizing EHR data for predictive modelin…
Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation
Jiaqi Wang, Yuzhong Chen, Yuhang Wu +3
Most existing personalized federated learning approaches are based on intricate designs, which often require complex implementation and tuning. In order to address this limitation,…
Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions
Suhan Cui, Jiaqi Wang, Yuan Zhong +3
The widespread adoption of Electronic Health Record (EHR) systems in healthcare institutes has generated vast amounts of medical data, offering significant opportunities for improv…
Hierarchical Pretraining on Multimodal Electronic Health Records
Xiaochen Wang, Junyu Luo, Jiaqi Wang +5
Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks. However, in the medical domai…
MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation
Yuan Zhong, Suhan Cui, Jiaqi Wang +7
Health risk prediction is one of the fundamental tasks under predictive modeling in the medical domain, which aims to forecast the potential health risks that patients may face in…
Towards Personalized Federated Learning via Heterogeneous Model Reassembly
Jiaqi Wang, Xingyi Yang, Suhan Cui +4
This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures.…
Prompt Engineering for Healthcare: Methodologies and Applications
Jiaqi Wang, Enze Shi, Sigang Yu +21
Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aim…
Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoT
Jiaqi Wang, Shenglai Zeng, Zewei Long +3
Federated learning (FL) enables multiple clients to train models collaboratively without sharing local data, which has achieved promising results in different areas, including the…
Towards Natural Language-Based Visualization Authoring
Yun Wang, Zhitao Hou, Leixian Shen +5
A key challenge to visualization authoring is the process of getting familiar with the complex user interfaces of authoring tools. Natural Language Interface (NLI) presents promisi…
FedCon: A Contrastive Framework for Federated Semi-Supervised Learning
Zewei Long, Jiaqi Wang, Yaqing Wang +2
Federated Semi-Supervised Learning (FedSSL) has gained rising attention from both academic and industrial researchers, due to its unique characteristics of co-training machine lear…
FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning
Liwei Che, Zewei Long, Jiaqi Wang +3
Federated Learning has shown great potentials for the distributed data utilization and privacy protection. Most existing federated learning approaches focus on the supervised setti…
Adaptively Optimize Content Recommendation Using Multi Armed Bandit Algorithms in E-commerce
Ding Xiang, Becky West, Jiaqi Wang +2
E-commerce sites strive to provide users the most timely relevant information in order to reduce shopping frictions and increase customer satisfaction. Multi armed bandit models (M…
Active Learning for Product Type Ontology Enhancement in E-commerce
Yun Zhu, Sayyed M. Zahiri, Jiaqi Wang +2
Entity-based semantic search has been widely adopted in modern search engines to improve search accuracy by understanding users' intent. In e-commerce, an accurate and complete pro…