Summary of Learning-augmented Algorithms with Explicit Predictors, by Marek Elias and Haim Kaplan and Yishay Mansour and Shay Moran
Learning-Augmented Algorithms with Explicit Predictors
by Marek Elias, Haim Kaplan, Yishay Mansour, Shay Moran
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS)
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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 explores novel approaches to online machine learning problems, where predictors learn from both past and present data. By unpacking the predictor and integrating the learning problem within the algorithmic challenge, this work designs online learning algorithms tailored for specific tasks. The authors focus on fundamental problems like caching and scheduling, which have been well-studied in the black-box setting. They introduce new algorithms that leverage explicit learning to optimize overall performance, achieving improved bounds over previous work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn better from past data and new information. It shows how to make predictions more accurate and reliable by letting machines learn as they receive more input. The researchers focus on important problems like scheduling and storing data efficiently. They develop new ways for machines to learn and improve, which could lead to better results in the future. |
Keywords
* Artificial intelligence * Machine learning * Online learning
