1 citations · 1 across the 7 of their papers we have counts for
8 papers
The LIRA-Ising Model: Estimating the boundaries of irregularly shaped X-ray sources
Kathryn McKeough, Vinay L. Kashyap, Aneta Siemiginowska +5
Mapping the boundary of an extended source is a key step in the study of its morphology. The background contamination and statistical fluctuations of typical astronomical images ma…
HyCoRA: Hyper-Contrastive Role-Adaptive Learning for Role-Playing
Shihao Yang, Zhicong Lu, Yong Yang +3
Multi-character role-playing aims to equip models with the capability to simulate diverse roles. Existing methods either use one shared parameterized module across all roles or ass…
Causal Clustering for Conditional Average Treatment Effects Estimation and Subgroup Discovery
Zilong Wang, Turgay Ayer, Shihao Yang
Estimating heterogeneous treatment effects is critical in domains such as personalized medicine, resource allocation, and policy evaluation. A central challenge lies in identifying…
Are Statistical Methods Obsolete in the Era of Deep Learning? A Study of ODE Inverse Problems
Skyler Wu, Shihao Yang, S. C. Kou
In the era of AI, neural networks have become increasingly popular for modeling, inference, and prediction, largely due to their potential for universal approximation. With the pro…
Restoring the Forecasting Power of Google Trends with Statistical Preprocessing
Candice Djorno, Mauricio Santillana, Shihao Yang
Google Trends reports how frequently specific queries are searched on Google over time. It is widely used in research and industry to gain early insights into public interest. Howe…
EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems
Jianhong Chen, Shihao Yang
Parameter estimation and trajectory reconstruction for data-driven dynamical systems governed by ordinary differential equations (ODEs) are essential tasks in fields such as biolog…
O-MAGIC: Online Change-Point Detection for Dynamic Systems
Yan Sun, Yeping Wang, Zhaohui Li +1
The capture of changes in dynamic systems, especially ordinary differential equations (ODEs), is an important and challenging task, with multiple applications in biomedical researc…
Coupled Integral PINN for Discontinuity
Yeping Wang, Shihao Yang
Physics-Informed Neural Networks (PINNs) solve forward PDEs by minimizing residual losses from the governing equations with initial and boundary conditions, but they often struggle…