58 citations · 80 across the 19 of their papers we have counts for
20 papers
Progress-Aware Distillation for Mitigating Stagnation in Small Language Model Agents
Yuanpu Cao, Saket Sathe, Hanyu Wang +3
While LLM-based agent systems have demonstrated remarkable proficiency in reasoning and solving complex real-world tasks, they also come with substantial token consumption and infe…
KAMR: Grounding Generation via Knowledge-Aligned Multi-hop Retrieval
Xiaochen Wang, Yuan Zhong, Haoyu Wang +2
Graph-based retrieval-augmented generation increasingly relies on multi-hop retrieval, where answering a query requires composing multiple connected knowledge-graph triplets. Howev…
GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models
Yuan Zhong, Chuanwei Ruan, Moein Hasani +3
The rapid growth of online grocery shopping requires recommendation systems that capture cyclical purchasing behavior and diverse user intents. Traditional item-level methods face…
Towards Robust Multimodal Large Language Models Against Jailbreak Attacks
Ziyi Yin, Yuanpu Cao, Han Liu +3
While multimodal large language models (MLLMs) have achieved remarkable success in recent advancements, their susceptibility to jailbreak attacks has come to light. In such attacks…
Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis
Jiaqi Wang, Ziyi Yin, Quanzeng You +2
Geographic health disparities pose a pressing global challenge, particularly in underserved regions of low- and middle-income nations. Addressing this issue requires a collaborativ…
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…
Generative AI in the Wild: Prospects, Challenges, and Strategies
Yuan Sun, Eunchae Jang, Fenglong Ma +1
Propelled by their remarkable capabilities to generate novel and engaging content, Generative Artificial Intelligence (GenAI) technologies are disrupting traditional workflows in m…
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models
Ziyi Yin, Muchao Ye, Tianrong Zhang +5
Visual Question Answering (VQA) is a fundamental task in computer vision and natural language process fields. Although the ``pre-training & finetuning'' learning paradigm significa…
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text
Han Liu, Zhi Xu, Xiaotong Zhang +5
Black-box hard-label adversarial attack on text is a practical and challenging task, as the text data space is inherently discrete and non-differentiable, and only the predicted la…
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…
Mitigating Pooling Bias in E-commerce Search via False Negative Estimation
Xiaochen Wang, Xiao Xiao, Ruhan Zhang +5
Efficient and accurate product relevance assessment is critical for user experiences and business success. Training a proficient relevance assessment model requires high-quality qu…
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…
VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models
Ziyi Yin, Muchao Ye, Tianrong Zhang +6
Vision-Language (VL) pre-trained models have shown their superiority on many multimodal tasks. However, the adversarial robustness of such models has not been fully explored. Exist…