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Abstract: We consider linear regression with covariates that are separable random elements in a general Hilbert space. We first develop a principal component analysis for…

Protein sequence data offer a massive natural experiment: across evolution, residue positions co-vary in ways that encode 3D structure, illuminate key evolutionary constraints, and reveal…

Abstract: We propose a tensor-based linear algebraic framework for density estimation and sampling. The method consists of two simple steps: first, smoothing the empirical density…

Abstract: Identifying covariates that modify treatment effects is a critical problem in causal inference. Yet existing data-adaptive methods lack rigorous error control, risking spurious findings…

Abstract: Modern decision-making systems, from online marketplaces to large language models (LLMs), increasingly rely on high-dimensional human feedback, where heterogeneous user preferences and massive feature…