Summary of Towards Bayesian Data Selection, by Julian Rodemann
Towards Bayesian Data Selection
by Julian Rodemann
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 This paper proposes a novel approach to machine learning by framing data selection as a decision problem within decision theory. By embedding the iterative addition of data into decision theory, the authors aim to find Bayes-optimal selections of data. The paper focuses on semi-supervised learning and derives the respective Bayes criterion for self-training in this setting. Empirical assessments show that deploying this criterion mitigates confirmation bias in generalized linear models, semi-parametric generalized additive models, and Bayesian neural networks on simulated and real-world data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper takes machine learning to a new level by thinking about how we choose what data to use when training models. Instead of just adding more data randomly, the authors want to find the best way to add data that makes our models work better. They do this by using decision theory, which is like making decisions based on probabilities. The paper shows that using this approach can help fix a problem called confirmation bias in certain types of models. This means that our models will be more accurate and less likely to get stuck in their ways. |
Keywords
» Artificial intelligence » Embedding » Machine learning » Self training » Semi supervised
