Contrastive balancing representation learning for heterogeneous dose-response curves estimation

M Zhu, A Wu, H Li, R Xiong, B Li, X Yang… - Proceedings of the …, 2024 - ojs.aaai.org
Estimating the individuals' potential response to varying treatment doses is crucial for
decision-making in areas such as precision medicine and management science. Most …

SPContrastNet: A Self-Paced Contrastive Learning Model for Few-Shot Text Classification

J Chen, R Zhang, X Jiang, C Hu - ACM Transactions on Information …, 2024 - dl.acm.org
Meta-learning has recently promoted few-shot text classification, which identifies target
classes based on information transferred from source classes through a series of small tasks …

Basis is also explanation: Interpretable Legal Judgment Reasoning prompted by multi-source knowledge

S Li, S Zhao, Z Zhang, Z Fang, W Chen… - Information Processing & …, 2025 - Elsevier
Abstract The task of Legal Judgment Prediction (LJP) aims to forecast case outcomes by
analyzing fact descriptions, playing a pivotal role in enhancing judicial system efficiency and …

CADLRA: A multi-charge prediction method based on the Criminal Act-Driven Law Retrieval Augmentation

J Feng, L Zhao, H Qin, Y Xu, Z Wang - Engineering Applications of Artificial …, 2024 - Elsevier
Abstract Legal Artificial Intelligence (Legal AI) has garnered significant attention in both
academic and industrial domains in recent years. However, most legal judgment prediction …

LegalDuet: Learning Effective Representations for Legal Judgment Prediction through a Dual-View Legal Clue Reasoning

P Liu, Z Liu, X Yi, L Yang, S Wang, Y Gu, G Yu… - arXiv preprint arXiv …, 2024 - arxiv.org
Most existing Legal Judgment Prediction (LJP) models focus on discovering the legal
triggers in the criminal fact description. However, in real-world scenarios, a professional …

MJP: A Meta-learning Approach for Chinese Legal Judgment Prediction

Y Lang, H Hou, W Chen, S Sun - CCF International Conference on Natural …, 2024 - Springer
Legal judgment prediction (LJP) is an important task in legal AI that aims to predict outcomes
based on the factual description of a case. Most current approaches are data-intensive and …