5 papers
AdaptICA: Data-Adaptive Transformation Learning for Independent Component Analysis
Lida Jalili, Jingyu Liu, Vince D. Calhoun +1
Independent component analysis (ICA) is widely used to recover latent structure from signal and imaging data, but standard ICA assumes that the observed measurement scale preserves…
Similarity-Based Prediction for Digital Twins: Panel Data, Theory, and Applications
Ruihang Han, Li-Hsiang Lin
Prediction from sequential panel data is central to digital-twin modeling, where new panels arrive over time and the predictive system is updated sequentially. Existing methods oft…
Scalable and Communication-Efficient Varying Coefficient Mixed Effect Models: Methodology, Theory, and Applications
Lida Chalangar Jalili Dehkharghani, Li-Hsiang Lin
Human migration exhibits complex spatiotemporal dependence driven by environmental and socioeconomic forces. Modeling such patterns at scale requires methods that accommodate many…
Sparse Deep Additive Model with Interactions: Enhancing Interpretability and Predictability
Yi-Ting Hung, Li-Hsiang Lin, Vince D. Calhoun
Recent advances in deep learning highlight the need for personalized models that can learn from small samples, handle high-dimensional features, and remain interpretable. To addres…
Deep P-Spline: Theory, Fast Tuning, and Application
Noah Yi-Ting Hung, Li-Hsiang Lin, Vince D. Calhoun
Deep neural networks (DNNs) have been widely applied to solve real-world regression problems. However, selecting optimal network structures remains a significant challenge. This st…