4 papers · 1 filter
Statistical-Neural Interaction Networks for Interpretable Mixed-Type Data Imputation
Ou Deng, Shoji Nishimura, Atsushi Ogihara +1
Real-world tabular databases routinely combine continuous measurements and categorical records, yet missing entries are pervasive and can distort downstream analysis. We propose St…
Auditable Unit-Aware Thresholds in Symbolic Regression via Logistic-Gated Operators
Ou Deng, Ruichen Cong, Jianting Xu +3
AI for health will only scale when models are not only accurate but also readable, auditable, and governable. Many clinical and public-health decisions hinge on numeric thresholds…
Evolutionary Causal Discovery with Relative Impact Stratification for Interpretable Data Analysis
Ou Deng, Shoji Nishimura, Atsushi Ogihara +1
This study proposes Evolutionary Causal Discovery (ECD) for causal discovery that tailors response variables, predictor variables, and corresponding operators to research datasets.…
Missing Data Imputation Based on Dynamically Adaptable Structural Equation Modeling with Self-Attention
Ou Deng, Qun Jin
Addressing missing data in complex datasets including electronic health records (EHR) is critical for ensuring accurate analysis and decision-making in healthcare. This paper propo…