Summary of Efficient Line Search For Optimizing Area Under the Roc Curve in Gradient Descent, by Jadon Fowler and Toby Dylan Hocking
Efficient line search for optimizing Area Under the ROC Curve in gradient descent
by Jadon Fowler, Toby Dylan Hocking
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes new efficient algorithms for optimizing linear models using Receiver Operating Characteristic (ROC) curves. The Area Under the Curve (AUC) is a common evaluation metric in binary classification and changepoint detection, but it’s difficult to use for learning because its derivative is zero almost everywhere. To address this issue, the authors introduce the Area Under Min (AUM) of false positive and false negative rates as a differentiable surrogate for AUC. The paper studies the piecewise linear/constant nature of the AUM/AUC and proposes new efficient algorithms for choosing the learning rate in gradient descent, achieving the same log-linear asymptotic time complexity as constant step size gradient descent while computing a complete representation of the AUM/AUC. Experimental results demonstrate the effectiveness of the proposed algorithm in binary classification problems and changepoint detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding the best way to train machines to make good decisions, like sorting pictures into categories or detecting changes in data. Right now, there’s a problem with how we evaluate these machine learning models. It’s hard to use the standard way of measuring their performance because it’s not easy to calculate. The researchers propose a new method that makes it easier and faster to find the best approach for training these machines. They tested this new method on some examples and showed that it works well. |
Keywords
» Artificial intelligence » Auc » Classification » Gradient descent » Machine learning
