Summary of Semf: Supervised Expectation-maximization Framework For Predicting Intervals, by Ilia Azizi et al.
SEMF: Supervised Expectation-Maximization Framework for Predicting Intervals
by Ilia Azizi, Marc-Olivier Boldi, Valérie Chavez-Demoulin
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Supervised Expectation-Maximization Framework (SEMF) is a novel approach for generating prediction intervals in datasets with complete or missing data. By extending the traditional Expectation-Maximization algorithm to a supervised context, SEMF leverages latent variable modeling for uncertainty estimation. Compared to quantile regression methods, SEMF often achieves narrower normalized prediction intervals and higher coverage rates across 11 tabular datasets. Moreover, SEMF can be integrated with machine learning models like gradient-boosted trees and neural networks, highlighting its practical applicability in uncertainty quantification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make predictions that also shows how certain you are about those predictions. It’s called the Supervised Expectation-Maximization Framework (SEMF). SEMF helps us understand how much we don’t know, which is important for making decisions when there’s uncertainty. The results show that SEMF works well and can be used with different types of machine learning models. |
Keywords
» Artificial intelligence » Machine learning » Regression » Supervised
