Abstract: Symbolic discovery, or learning compact mathematical expressions from data, offers a form of interpretability in which the fitted model is itself a scientific statement.…
Abstract: Precision medicine aims to transform complex patient data and clinical knowledge into individualized treatment decisions. However, this remains challenging when treatments are sequential, patient…
Abstract: This study introduces Variational Automatic Relevance Determination (VARD), a novel approach for fitting sparse additive regression models in high-dimensional settings. VARD stands out by…
Abstract: Two-sample testing is a fundamental problem in statistics, but detecting general distributional differences becomes increasingly challenging in high dimensions, where classical distance-based methods can…
Abstract: The Network Scale-Up Method (NSUM) is a vital tool for estimating the sizes of hard-to-reach populations using Aggregated Relational Data (ARD). Recent advancements in…
Abstract: Competitor rating systems for head-to-head games are typically used to measure playing strength from game outcomes. Ratings computed from these systems are often used…
Abstract: Contemporary data often have a matrix form, with intrinsic information embedded in the data structure, and are high-dimensional. The matrix Gaussian graphical model is…