Summary of Quantifying the Prediction Uncertainty Of Machine Learning Models For Individual Data, by Koby Bibas
Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data
by Koby Bibas
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT)
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 Machine learning models have achieved remarkable success across numerous domains, with empirical risk minimization (ERM) being a widely employed approach. However, ERM relies on the assumption that the test distribution is similar to the training distribution, which may not always hold true in real-world scenarios. In contrast, predictive normalized maximum likelihood (pNML) has been proposed as a min-max solution for individual settings where no distributional assumptions are made. This study investigates pNML’s learnability for linear regression and neural networks, demonstrating its ability to improve model performance and robustness on various tasks. Additionally, pNML provides an accurate confidence measure for its output, showcasing state-of-the-art results in out-of-distribution detection, resistance to adversarial attacks, and active learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can do amazing things! This paper talks about a new way of training models called predictive normalized maximum likelihood (pNML). It’s different from the usual way we train models because it doesn’t assume that the test data is similar to the training data. The study shows that pNML can help models learn better and be more robust, which means they can handle unexpected situations. It also does a great job of predicting how certain its answers are, which is really important for things like detecting when something is outside the normal range or protecting against fake data. |
Keywords
» Artificial intelligence » Active learning » Likelihood » Linear regression » Machine learning
