# Multivariate and Online Transfer Learning with Uncertainty Quantification

arXiv: https://arxiv.org/abs/2411.12555 · HTML: https://mlsanity.com/paper/2411.12555/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 12 of 12)

- [Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters](https://mlsanity.com/paper/2403.11549/) (2024)
- [The effectiveness of MAE pre-pretraining for billion-scale pretraining](https://mlsanity.com/paper/2303.13496/) (2023)
- [Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors](https://mlsanity.com/paper/2205.10279/) (2022)
- [Bayesian Optimization with High-Dimensional Outputs](https://mlsanity.com/paper/2106.12997/) (2021)
- [Variational Auto-Regressive Gaussian Processes for Continual Learning](https://mlsanity.com/paper/2006.05468/) (2020)
- [Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach](https://mlsanity.com/paper/2108.12112/) (2021)
- [Semi-supervised Triply Robust Inductive Transfer Learning](https://mlsanity.com/paper/2209.04977/) (2022)
- [Federated Online and Bandit Convex Optimization](https://mlsanity.com/paper/2311.17586/) (2023)
- [Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)](https://mlsanity.com/paper/2211.16557/) (2022)
- [A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms](https://mlsanity.com/paper/2109.01377/) (2021)
- [Bayesian Transfer Learning](https://mlsanity.com/paper/2312.13484/) (2023)
- [A Bayesian shrinkage estimator for transfer learning](https://mlsanity.com/paper/2403.17321/) (2024)

## Abstract (arXiv)

Untreated periodontitis causes inflammation within the supporting tissue of the teeth and can ultimately lead to tooth loss. Modeling periodontal outcomes is beneficial as they are difficult and time consuming to measure, but disparities in representation between demographic groups must be considered. There may not be enough participants to build group specific models and it can be ineffective, and even dangerous, to apply a model to participants in an underrepresented group if demographic differences were not considered during training. We propose an extension to RECaST Bayesian transfer learning framework. Our method jointly models multivariate outcomes, exhibiting significant improvement over the previous univariate RECaST method. Further, we introduce an online approach to model sequential data sets. Negative transfer is mitigated to ensure that the information shared from the other demographic groups does not negatively impact the modeling of the underrepresented participants. The Bayesian framework naturally provides uncertainty quantification on predictions. Especially important in medical applications, our method does not share data between domains. We demonstrate the effectiveness of our method in both predictive performance and uncertainty quantification on simulated data and on a database of dental records from the HealthPartners Institute.