Summary of Learning to Cover: Online Learning and Optimization with Irreversible Decisions, by Alexandre Jacquillat et al.
Learning to Cover: Online Learning and Optimization with Irreversible Decisions
by Alexandre Jacquillat, Michael Lingzhi Li
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 defines an online learning problem where irreversible decisions are made to achieve a coverage target. At each period, a decision-maker selects facilities to open, receives feedback on their success, and updates a machine learning model to guide future decisions. The goal is to minimize costs across a finite horizon while meeting the coverage target with high probability. The authors derive an optimal algorithm and a tight lower bound in the asymptotic regime where the number of facilities grows large but the horizon remains finite. They show that the regret grows sub-linearly at a rate proportional to the square root of the number of facilities, indicating exponential convergence. The results are robust to different learning environments and can be extended to more complex facility location settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making smart decisions online without being able to change them later. Imagine you’re choosing which stores to open in a new city, and each one has the potential to attract customers. You want to make sure most people are covered by your stores. The authors come up with an algorithm that helps you do this while keeping costs low. They show that even after making just a few decisions, their approach can lead to significant improvements compared to not using any learning at all. This is important because it means you don’t need to wait until the end of the process to see the benefits. |
Keywords
* Artificial intelligence * Machine learning * Online learning * Probability
