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: Regulatory compliance increasingly relies on the labor of producing, managing, and transmitting digital information—a form of work largely invisible to administrative law. Although agencies…
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: A natural question in the deployment of modern machine learning models (such as LLMs) is whether they can accurately self-assess confidence in their predictions…
Abstract: Generative AI offers a powerful way to create synthetic data for simulation, training, and evaluation. But what if a model generates outputs with an…
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: Despite recent efforts in using large language models (LLMs) to plan as agents, their hallucination and lack of verifiability undermine executability and trust, preventing…
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…
Abstract: Many problems from a wide variety of areas can be formulated mathematically as the problem of computing a fixed point of a suitable given…