arXiv Machine Learning June 30, 2026 By Mitchell L. Prevett, Francis K. C. Hui, Zhi Yang Tho, A. H. Welsh, Anton H. Westveld

Gradient Boosted Mixed Models: Flexible Estimation of Mean and Variance Components for Clustered Data

Read the original on arXiv Machine Learning →

arXiv:2511. 00217v2 Announce Type: replace-cross Abstract: We introduce Gradient Boosted Mixed Models (GBMixed), a framework which extends boosting to clustered data by jointly modeling the mean and variance components in a linear mixed model via likelihood-based gradients.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment

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arXiv Machine Learning

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arXiv Machine Learning

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Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Data

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arXiv Machine Learning

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arXiv Machine Learning

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arXiv Machine Learning

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Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation

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Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment

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