Summary of Conformal Online Model Aggregation, by Matteo Gasparin and Aaditya Ramdas
Conformal online model aggregation
by Matteo Gasparin, Aaditya Ramdas
First submitted to arxiv on: 22 Mar 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 proposed approach to conformal model aggregation in online settings combines the prediction sets from multiple machine learning algorithms using a weighted voting scheme. This method adapts the weights on each algorithm based on its past performance, allowing for a reasonable notion of uncertainty quantification without making strong distributional assumptions. The approach is particularly useful when selecting the best-performing model among a pool of candidates, and can be applied to various prediction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are getting better at predicting what will happen next. But they’re not always sure. That’s where “conformal prediction” comes in – it gives us an idea of how certain we should be about their predictions. The problem is that this works only if you know which model to use from the start. This paper figures out a way to combine multiple models, like random forests and neural nets, to get a better picture of what’s likely to happen. |
Keywords
* Artificial intelligence * Machine learning
