Summary of Non-clairvoyant Scheduling with Partial Predictions, by Ziyad Benomar et al.
Non-clairvoyant Scheduling with Partial Predictions
by Ziyad Benomar, Vianney Perchet
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This research paper explores the non-clairvoyant scheduling problem, where an algorithm makes decisions based on predictions without guaranteed quality. The investigation focuses on scenarios where only limited prediction data is available, which can be due to cost or data limitations. The study establishes near-optimal lower bounds and algorithms for perfect predictions and presents a learning-augmented algorithm that meets criteria for robustness, consistency, and smoothness. This tradeoff between consistency and smoothness has not been explored before in this scenario with limited prediction data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about an important problem where computers make decisions based on uncertain information. They can’t see the future perfectly, so they have to make do with what they have. The researchers looked at how well algorithms can work when they only have some of the information they need. They found new ways to make sure these algorithms are good and consistent, even when they don’t have all the data. |
