14 citations · 41 across the 14 of their papers we have counts for
14 papers
MEME: Modeling the Evolutionary Modes of Financial Markets
Taian Guo, Haiyang Shen, Junyu Luo +7
LLMs have demonstrated significant potential in quantitative finance by processing vast unstructured data to emulate human-like analytical workflows. However, current LLM-based met…
AlphaPROBE: Alpha Mining via Principled Retrieval and On-graph biased evolution
Taian Guo, Haiyang Shen, Junyu Luo +6
Extracting signals through alpha factor mining is a fundamental challenge in quantitative finance. Existing automated methods primarily follow two paradigms: Decoupled Factor Gener…
AlphaSAGE: Structure-Aware Alpha Mining via GFlowNets for Robust Exploration
Binqi Chen, Hongjun Ding, Ning Shen +4
The automated mining of predictive signals, or alphas, is a central challenge in quantitative finance. While Reinforcement Learning (RL) has emerged as a promising paradigm for gen…
AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining
Hongjun Ding, Binqi Chen, Jinsheng Huang +6
Formula alpha mining, which generates predictive signals from financial data, is critical for quantitative investment. Although various algorithmic approaches-such as genetic progr…
MASS: Muli-agent simulation scaling for portfolio construction
Taian Guo, Haiyang Shen, JinSheng Huang +9
The application of LLM-based agents in financial investment has shown significant promise, yet existing approaches often require intermediate steps like predicting individual stock…
MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation
Jinsheng Huang, Liang Chen, Taian Guo +13
Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, often assessed through multiple-choice questions (MCQs) that include an image,…
Redundancy-Free Self-Supervised Relational Learning for Graph Clustering
Si-Yu Yi, Wei Ju, Yifang Qin +4
Graph clustering, which learns the node representations for effective cluster assignments, is a fundamental yet challenging task in data analysis and has received considerable atte…
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts
Siyu Yi, Zhengyang Mao, Wei Ju +4
Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-qualit…
Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation
Xiao Luo, Daqing Wu, Yiyang Gu +7
Recent years have witnessed the explosive growth of interaction behaviors in multimedia information systems, where multi-behavior recommender systems have received increasing atten…
Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases
Luchen Liu, Zequn Liu, Haoxian Wu +4
Mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare. However, data insufficiency and the clinical d…
Predictive Multi-level Patient Representations from Electronic Health Records
Zichang Wang, Haoran Li, Luchen Liu +2
The advent of the Internet era has led to an explosive growth in the Electronic Health Records (EHR) in the past decades. The EHR data can be regarded as a collection of clinical e…
Early Prediction of Sepsis From Clinical Datavia Heterogeneous Event Aggregation
Luchen Liu, Haoxian Wu, Zichang Wang +2
Sepsis is a life-threatening condition that seriously endangers millions of people over the world. Hopefully, with the widespread availability of electronic health records (EHR), p…
Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction
Luchen Liu, Haoran Li, Zhiting Hu +4
Clinical outcome prediction based on the Electronic Health Record (EHR) plays a crucial role in improving the quality of healthcare. Conventional deep sequential models fail to cap…
Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction
Luchen Liu, Jianhao Shen, Ming Zhang +2
The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is…