Summary of Probabilistic Calibration by Design For Neural Network Regression, By Victor Dheur et al.
Probabilistic Calibration by Design for Neural Network Regression
by Victor Dheur, Souhaib Ben Taieb
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 tackles the issue of neural network miscalibration for regression problems, which can lead to suboptimal decision-making. The authors propose a novel end-to-end training procedure called Quantile Recalibration Training that integrates post-hoc calibration directly into the training process without additional parameters. This method is part of a unified algorithm that includes various post-hoc and regularization methods as special cases. The authors demonstrate the effectiveness of their approach in a large-scale experiment involving 57 tabular regression datasets, achieving improved predictive accuracy while maintaining calibration. They also conduct an ablation study to evaluate the significance of different components within their proposed method and analyze the impact of base models and hyperparameters on predictive accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make neural networks more accurate by fixing a problem called miscalibration. Miscalibration can lead to bad decisions in many real-world applications that use regression, like predicting house prices or stock prices. The authors created a new way to train neural networks that includes calibration right from the start, without needing extra steps or parameters. They tested this method on 57 different datasets and showed it works better than other methods. They also looked at how different parts of their method worked together and what factors affected its performance. |
Keywords
* Artificial intelligence * Neural network * Regression * Regularization
