Summary of Generalization Error Of the Tilted Empirical Risk, by Gholamali Aminian et al.
Generalization Error of the Tilted Empirical Risk
by Gholamali Aminian, Amir R. Asadi, Tian Li, Ahmad Beirami, Gesine Reinert, Samuel N. Cohen
First submitted to arxiv on: 28 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Information Theory (cs.IT); Machine Learning (cs.LG)
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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 proposes a novel non-linear risk metric for machine learning applications, building upon the idea of exponential tilting. The tilted empirical risk aims to quantify the prediction ability of supervised statistical learning algorithms on previously unseen data. The authors provide uniform and information-theoretic bounds on the generalization error of this risk metric, with a convergence rate of O(1/√n), where n is the number of training samples. Additionally, they explore the solution to the KL-regularized expected tilted empirical risk minimization problem and derive an upper bound on the expected generalization error with a convergence rate of O(1/n). This work has implications for machine learning applications such as classification and regression problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how well machine learning algorithms can predict things they haven’t seen before. It creates a new way to measure this prediction ability, called the tilted empirical risk. The authors show that their new metric is a good predictor of an algorithm’s performance on new data. They also look at a problem where we try to find the best solution to minimize the difference between the predicted and actual values. |
Keywords
» Artificial intelligence » Classification » Generalization » Machine learning » Regression » Supervised
