Ying Sun

Towards Faithful Neural Network Intrinsic Interpretation with Shapley Additive Self-Attribution

2023 · arXiv (Cornell University)

Self-interpreting neural networks have garnered significant interest in research. Existing works in this domain often (1) lack a solid theoretical foundation ensuring genuine interpretability or (2) compromise model expressiveness. In response, we formulate a generic …

Collaboration-Aware Hybrid Learning for Knowledge Development Prediction

2024

In recent years, the rise of online Knowledge Management Systems (KMSs) has significantly improved work efficiency in enterprises. Knowledge development prediction, as a critical application within these online platforms, enables organizations to proactively address knowledge …

Multi-round Counterfactual Generation: Interpreting and Improving Models of Text Classification

2024

In recent years, natural language processing (NLP) models have demonstrated remarkable performance in text classification tasks. However, trust in the decision-making process requires a deeper understanding of the operational principles of these networks. Therefore, there …

A Comprehensive Survey on Self-Interpretable Neural Networks

2025 · arXiv (Cornell University)

Neural networks have achieved remarkable success across various fields. However, the lack of interpretability limits their practical use, particularly in critical decision-making scenarios. Post-hoc interpretability, which provides explanations for pre-trained models, is often at risk …

Improving the prediction accuracy of statistical models: A new hierarchical clustering approach

2025 · Statistics and Computing

Statisticians and machine learning practitioners frequently encounter datasets originated from multiple populations but containing the same type of measurements. In such cases, predictive analytics is typically carried out by either fitting a separate model to …