Summary of Online Classification with Predictions, by Vinod Raman et al.
Online Classification with Predictions
by Vinod Raman, Ambuj Tewari
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); 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 A novel online learner is designed to tackle online classification tasks when the quality of future examples’ predictions is available. This learner’s expected regret never exceeds its worst-case counterpart, and it can significantly outperform this bound if future example predictions are accurate. A corollary shows that if the learner always observes predictable data, online learning becomes as easy as transductive online learning. These results complement recent work in online algorithms with predictions and smoothed online classification, which leverage machine-learned predictions and distributional assumptions to improve performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary When we have information about what’s coming next, like a weather forecast, it can help us make better decisions. This paper shows how to use this idea to improve our ability to classify things online, without having to look at all the data beforehand. The method is designed to work well even if the predictions are not always accurate, and it can be more effective than other approaches when the predictions are good. |
Keywords
» Artificial intelligence » Classification » Online learning
