Summary of Learning Submodular Sequencing From Samples, by Jing Yuan et al.
Learning Submodular Sequencing from Samples
by Jing Yuan, Shaojie Tang
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents an algorithm for sequential submodular maximization, which is crucial in various real-world scenarios like ranking products online. Unlike previous works that assume access to the utility function, this approach relies on samples with associated utilities. The algorithm requires polynomially many samples drawn from a two-stage uniform distribution and achieves an approximation ratio dependent on individual submodular functions’ curvature. This means it can be applied in various contexts where complete knowledge of the utility function is unavailable. The results extend prior work on “optimization from samples” by generalizing from optimizing set functions to sequence-dependent functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in computer science: figuring out how to rank things in order, like products online. Instead of needing all the information about each product, we only need some examples or samples with the products and their usefulness ratings. The new algorithm is really good at this job and can be used in many real-life situations where we don’t have all the details. It’s an important breakthrough that shows how limited data can still help us make smart decisions. |
Keywords
» Artificial intelligence » Optimization
