Summary of Beyond Calibration: Assessing the Probabilistic Fit Of Neural Regressors Via Conditional Congruence, by Spencer Young et al.
Beyond Calibration: Assessing the Probabilistic Fit of Neural Regressors via Conditional Congruence
by Spencer Young, Cole Edgren, Riley Sinema, Andrew Hall, Nathan Dong, Porter Jenkins
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper addresses a common issue in deep learning models that predict uncertainty. Despite recent advancements in specifying neural networks capable of representing uncertainty, existing approaches often suffer from overconfidence and misaligned predictive distributions. The authors propose a new metric, Conditional Congruence Error (CCE), which uses conditional kernel mean embeddings to estimate the distance between learned predictive distributions and empirical, conditional distributions in a dataset. This allows for point-wise reliability assessment, crucial for real-world decision-making. The proposed method shows accurate quantification of misalignment when the data generating process is known, effective scaling to high-dimensional image regression tasks, and reliable model evaluation on unseen instances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models that predict uncertainty can be overconfident and misaligned. This makes it hard to trust their predictions. Researchers have made progress in this area, but there’s still a problem. A new metric is proposed to fix this issue. It compares the predicted distribution with what actually happens in the data. This helps determine if the model is reliable on new, unseen instances. The new method works well even when dealing with large amounts of image data and is useful for real-world decisions. |
Keywords
» Artificial intelligence » Deep learning » Regression
