Summary of Robust Conformal Prediction Using Privileged Information, by Shai Feldman et al.
Robust Conformal Prediction Using Privileged Information
by Shai Feldman, Yaniv Romano
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel method for generating prediction sets with guaranteed coverage rates is developed in this paper. The approach builds on conformal prediction, but adapts it to be robust to corruptions in the training data, such as missing or noisy variables. This is achieved by introducing a new generalization of weighted conformal prediction, which assumes access to privileged information (PI) during training and incorporates distribution shift explanations. Theoretical coverage guarantees are provided for this method, demonstrating its validity. Empirical experiments on real and synthetic datasets show that the approach achieves valid coverage rates and constructs more informative predictions compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to make predictions with a high level of accuracy. It’s like having a safety net to catch any mistakes in your predictions. The method uses something called conformal prediction, which is good at making predictions when the data is similar. But when there are problems with the data, like missing or noisy variables, this approach doesn’t work well. To fix this, the paper introduces a new way of using privileged information (PI) to understand why the predictions went wrong. This helps the method make better predictions and be more reliable. |
Keywords
» Artificial intelligence » Generalization
