AI RESEARCH
Scalable Krylov Subspace Methods for Generalized Mixed-Effects Models with Crossed Random Effects
arXiv CS.LG
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ArXi:2505.09552v3 Announce Type: replace-cross Mixed-effects models are widely used to model data with hierarchical grouping structures and high-cardinality categorical predictor variables. However, for high-dimensional crossed random effects, current standard computations relying on Cholesky decompositions can become prohibitively slow. In this work, we present Krylo subspace-based methods that address existing computational bottlenecks, and we analyze them both theoretically and empirically.