Summary of Leveraging Gradients For Unsupervised Accuracy Estimation Under Distribution Shift, by Renchunzi Xie et al.
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift
by Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko, Jianfeng Zhang, Bo An
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 This paper tackles the crucial problem of estimating test accuracy without ground-truth labels in varying environments, a challenge for safe machine learning deployment. Existing methods rely on neural network outputs or features to estimate this score. The authors explore the use of gradients to predict test accuracy even under distribution shifts. They propose using the norm of classification-layer gradients, backpropagated from the cross-entropy loss after one gradient step over test data. This approach adjusts the model’s magnitude according to its generalization abilities. Theoretical insights and empirical experiments demonstrate that this method outperforms state-of-the-art algorithms on diverse distribution shifts and model structures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine learning models work better in new situations without knowing how well they will perform beforehand. Right now, scientists have to test their models many times to figure this out. The authors of this paper found a way to use the “gradients” (a measure of how much the model changes when given different data) to predict how well the model will do in new situations. They tested this idea and showed that it works better than other methods. |
Keywords
* Artificial intelligence * Classification * Cross entropy * Generalization * Machine learning * Neural network
