# Statistical Model Aggregation via Parameter Matching

arXiv: https://arxiv.org/abs/1911.00218 · HTML: https://mlsanity.com/paper/1911.00218/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2019.

## Built on by (most cited first, 17 of 17)

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- [Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data](https://mlsanity.com/paper/2602.07081/) (2026)
- [Bayesian Federated Neural Matching that Completes Full Information](https://mlsanity.com/paper/2211.08010/) (2022)
- [Investigating Neuron Disturbing in Fusing Heterogeneous Neural Networks](https://mlsanity.com/paper/2210.12974/) (2022)
- [The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning](https://mlsanity.com/paper/2312.03120/) (2023)
- [Model Merging as Probabilistic Inference in Fine-Tuning Parameter Space](https://mlsanity.com/paper/2607.01689/) (2026)
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## Abstract (arXiv)

We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets. Exploiting tools from Bayesian nonparametrics, we develop a general meta-modeling framework that learns shared global latent structures by identifying correspondences among local model parameterizations. Our proposed framework is model-independent and is applicable to a wide range of model types. After verifying our approach on simulated data, we demonstrate its utility in aggregating Gaussian topic models, hierarchical Dirichlet process based hidden Markov models, and sparse Gaussian processes with applications spanning text summarization, motion capture analysis, and temperature forecasting.