Summary of Online Structured Prediction with Fenchel–young Losses and Improved Surrogate Regret For Online Multiclass Classification with Logistic Loss, by Shinsaku Sakaue et al.
Online Structured Prediction with Fenchel–Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss
by Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 extends the “exploit-the-surrogate-gap” framework for online structured prediction, previously limited to multiclass classification. The framework relies on Fenchel-Young losses, which include logistic loss as a special case. To convert estimated scores to outputs, the authors propose randomized decoding and analyze its performance in various structured prediction problems. In online multiclass classification with logistic loss, they achieve a surrogate regret bound of O(||U||F^2), improving previous bounds by a factor of d, the number of classes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making predictions on things like images or text without having all the information at once. It’s trying to figure out how to do this efficiently and accurately. The authors are using a special method called “exploit-the-surrogate-gap” that works well for some types of problems, but not others. They’re extending this method to work with more kinds of data, like structured output problems. This will help make predictions better and faster. |
Keywords
* Artificial intelligence * Classification
