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A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning Ensemble Distributionally Robust Bayesian Optimisation The Proxy Presumption: From Semantic Embeddings to Valid Social Measures Modulated learning for private and distributed regression with just a single sample per client device Query-efficient model evaluation using cached responses Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks Optimal Experiments for Partial Causal Effect Identification Order-Agnostic Autoregressive Modelling with Missing Data Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes Tuning Derivatives for Causal Fairness in Machine Learning Spherical Flows for Sampling Categorical Data Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors GRALIS: A Unified Canonical Framework for Linear Attribution Methods via Riesz Representation Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics Perturbation is All You Need for Extrapolating Language Models Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization Realizable Bayes-Consistency for General Metric Losses Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution Segmenting Human-LLM Co-authored Text via Change Point Detection Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation Understanding Self-Supervised Learning via Latent Distribution Matching The Geometric Mechanics of Contrastive Representation Learning: Alignment Potentials, Entropic Dispersion, and Cross-modal Divergence Imbalanced Classification under Capacity Constraints On the Spectral Structure and Objective Equivalence of Orthogonal Multilabel Fisher Discriminants Partially Observed Structural Causal Models First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint Robust and Fast Training via Per-Sample Clipping
Scalable and Communication-Efficient Varying Coefficient ...
[Submitted on 16 Nov 2025 (v1), last revised 28 May 2026 (this v · 2026-05-29 · via stat updates on arXiv.org

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Abstract:Human migration exhibits complex spatiotemporal dependence driven by environmental and socioeconomic forces. Modeling such patterns at scale requires methods that accommodate many random effects while remaining feasible when raw data or large design matrices cannot be freely shared across distributed nodes. We develop a communication-efficient inference framework for Varying Coefficient Mixed Models (VCMMs) with flexible mean structures and large correlated random-effect components. Using a Bayesian hierarchical representation of penalized splines, we derive sufficient statistics that preserve each node's likelihood contribution and recover the estimator from the full data under unrestricted communication. Under communication constraints, these statistics support a one-step communication-efficient estimator with first-order efficiency. An SVD-enhanced implementation stabilizes large or ill-conditioned random-effect covariance operators. Theory establishes likelihood preservation, convergence, asymptotic efficiency, and finite-sample concentration. Simulations and U.S. migration-flow data demonstrate accuracy, scalability, and recovery of dynamic spatial patterns.

Submission history

From: Li-Hsiang Lin [view email]
[v1] Sun, 16 Nov 2025 18:58:31 UTC (1,091 KB)
[v2] Wed, 20 May 2026 18:06:30 UTC (1,408 KB)
[v3] Thu, 28 May 2026 16:32:54 UTC (1,409 KB)