3 papers
cs.LG2025
Implet: A Post-hoc Subsequence Explainer for Time Series Models
Fanyu Meng, Ziwen Kan, Shahbaz Rezaei +3
Explainability in time series models is crucial for fostering trust, facilitating debugging, and ensuring interpretability in real-world applications. In this work, we introduce Im…
cs.LG2024
CohEx: A Generalized Framework for Cohort Explanation
Fanyu Meng, Xin Liu, Zhaodan Kong +1
eXplainable Artificial Intelligence (XAI) has garnered significant attention for enhancing transparency and trust in machine learning models. However, the scopes of most existing e…
cs.LG2024
Interpreting Inflammation Prediction Model via Tag-based Cohort Explanation
Fanyu Meng, Jules Larke, Xin Liu +4
Machine learning is revolutionizing nutrition science by enabling systems to learn from data and make intelligent decisions. However, the complexity of these models often leads to…