1 citations · 1 across the 6 of their papers we have counts for
7 papers
Disentangling and Fusing Neurostructural and Vascular Ageing for Retinal Age Prediction
Junwen Zheng, Li Rong Wang, Anthony Zihan Lin +6
Estimating biological age is an important task in ageing research, as it quantifies individual ageing trajectories beyond chronological age. Retinal age estimation has become a wel…
Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading
Lin Li, Li Rong Wang, Hsuan Fu +1
Reinforcement Learning (RL) has emerged as a powerful approach in financial trading, enabling agents to learn optimal strategies through direct market interaction. However, financi…
CUPID: A Plug-in Framework for Joint Aleatoric and Epistemic Uncertainty Estimation with a Single Model
Xinran Xu, Xiuyi Fan
Accurate estimation of uncertainty in deep learning is critical for deploying models in high-stakes domains such as medical diagnosis and autonomous decision-making, where overconf…
Robust Uncertainty Estimation under Distribution Shift via Difference Reconstruction
Xinran Xu, Li Rong Wang, Xiuyi Fan
Estimating uncertainty in deep learning models is critical for reliable decision-making in high-stakes applications such as medical imaging. Prior research has established that the…
Explainable Melanoma Diagnosis with Contrastive Learning and LLM-based Report Generation
Junwen Zheng, Xinran Xu, Li Rong Wang +5
Deep learning has demonstrated expert-level performance in melanoma classification, positioning it as a powerful tool in clinical dermatology. However, model opacity and the lack o…
Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discovery
Woon Yee Ng, Li Rong Wang, Siyuan Liu +1
Explaining machine learning (ML) predictions has become crucial as ML models are increasingly deployed in high-stakes domains such as healthcare. While SHapley Additive exPlanation…
Stock Price Predictability and the Business Cycle via Machine Learning
Li Rong Wang, Hsuan Fu, Xiuyi Fan
We study the impacts of business cycles on machine learning (ML) predictions. Using the S&P 500 index, we find that ML models perform worse during most recessions, and the inclusio…