Summary of Competing Bandits in Decentralized Large Contextual Matching Markets, by Satush Parikh et al.
Competing Bandits in Decentralized Large Contextual Matching Markets
by Satush Parikh, Soumya Basu, Avishek Ghosh, Abishek Sankararaman
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper explores decentralized learning in two-sided matching markets where demand-side agents compete for a large supply side with time-varying preferences. The goal is to achieve a stable match despite the inefficiencies of existing algorithms like Explore-Then-Commit and Upper-Confidence-Bound, which scale linearly with the number of arms (K). Building upon the linear contextual bandit framework, the authors assume that each agent’s arm-mean can be represented by a linear function of a known feature vector and an unknown parameter. The authors aim to develop more efficient learning algorithms for this problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can help people find good matches in situations where many things are being compared. Right now, some computer programs aren’t very good at this because they take too long or make bad choices. The researchers want to improve these programs by giving them more information and making them smarter. They think that if each person’s preferences can be described using a simple formula, the program will be able to find better matches faster. |
