Summary of Online Stackelberg Optimization Via Nonlinear Control, by William Brown et al.
Online Stackelberg Optimization via Nonlinear Control
by William Brown, Christos Papadimitriou, Tim Roughgarden
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT)
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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 presents a unified algorithmic framework for solving repeated interaction problems with adaptive agents. It shows that many such problems can be cast as online control problems satisfying local controllability, and introduces an algorithm for tractable regret minimization. The framework is efficient when the instance dynamics are known or locally action-linear, and provides tight bounds in the presence of adversarial disturbances. The paper also presents sublinear regret results for unknown dynamics and bandit feedback settings, with applications to performative prediction, recommendations, adaptive pricing, and repeated gameplay. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us better understand how machines can make decisions when they interact with each other. It shows that many problems of this kind can be solved using a new approach called online control. This approach allows for efficient decision-making while considering the possible actions of the other machines. The paper also provides guarantees on the quality of these decisions in different scenarios, such as when there are disturbances or unknown dynamics. The results have applications in areas like personalized recommendations and game playing. |
