English

Towards Bayesian Data Selection

Machine Learning 2024-06-25 v2 Artificial Intelligence Machine Learning Statistics Theory Statistics Theory

Abstract

A wide range of machine learning algorithms iteratively add data to the training sample. Examples include semi-supervised learning, active learning, multi-armed bandits, and Bayesian optimization. We embed this kind of data addition into decision theory by framing data selection as a decision problem. This paves the way for finding Bayes-optimal selections of data. For the illustrative case of self-training in semi-supervised learning, we derive the respective Bayes criterion. We further show that deploying this criterion mitigates the issue of confirmation bias by empirically assessing our method for generalized linear models, semi-parametric generalized additive models, and Bayesian neural networks on simulated and real-world data.

Keywords

Cite

@article{arxiv.2406.12560,
  title  = {Towards Bayesian Data Selection},
  author = {Julian Rodemann},
  journal= {arXiv preprint arXiv:2406.12560},
  year   = {2024}
}

Comments

5th Workshop on Data-Centric Machine Learning Research (DMLR) at ICML 2024