activity
20222026
most citedMCare: Learning with Missing Modalities in Multimodal Healthcare Data

89 citations · 91 across the 7 of their papers we have counts for

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8 papers · 1 filter

cs.LG2026

The Weakest Link Tells It All: Outcome-Supervised Process Reward Modeling via Learnable Credit Assignment

Tianyu Jia, Yue Fang, Hongxin Ding +6

Process reward models (PRMs) enhance the reasoning capabilities of large language models (LLMs) by providing fine-grained feedback, yet training PRMs typically requires expensive s…

cs.LG2025

Bridging Global Intent with Local Details: A Hierarchical Representation Approach for Semantic Validation in Text-to-SQL

Rihong Qiu, Zhibang Yang, Xinke Jiang +5

Text-to-SQL translates natural language questions into SQL statements grounded in a target database schema. Ensuring the reliability and executability of such systems requires vali…

cs.LG2025

DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Search

Zhibang Yang, Xinke Jiang, Rihong Qiu +8

Federated Retrieval (FR) routes queries across multiple external knowledge sources, to mitigate hallucinations of LLMs, when necessary external knowledge is distributed. However, e…

cs.LG2024

RAGraph: A General Retrieval-Augmented Graph Learning Framework

Xinke Jiang, Rihong Qiu, Yongxin Xu +7

Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs…

cs.LG2024

3DS: Medical Domain Adaptation of LLMs via Decomposed Difficulty-based Data Selection

Hongxin Ding, Yue Fang, Runchuan Zhu +6

Large Language Models(LLMs) excel in general tasks but struggle in specialized domains like healthcare due to limited domain-specific knowledge.Supervised Fine-Tuning(SFT) data con…

cs.LG2024

IntelliCare: Improving Healthcare Analysis with Variance-Controlled Patient-Level Knowledge from Large Language Models

Zhihao Yu, Yujie Jin, Yongxin Xu +3

While pioneering deep learning methods have made great strides in analyzing electronic health record (EHR) data, they often struggle to fully capture the semantics of diverse medic…