Summary of Tight Rates For Bandit Control Beyond Quadratics, by Y. Jennifer Sun et al.
Tight Rates for Bandit Control Beyond Quadratics
by Y. Jennifer Sun, Zhou Lu
First submitted to arxiv on: 1 Oct 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 The paper proposes a novel approach to tackle highly complex real-world control problems that involve adversarial perturbations, bandit feedback models, and non-quadratic cost functions. Unlike classical control theories like Linear Quadratic Control (LQC), this method can achieve optimal regret in these general control problems. The authors reduce the problem to bandit convex optimization with memory and develop a gradient estimator with low variance to address the challenges posed by the memory structure and non-quadratic loss functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making it possible to control complex systems that are affected by bad events or unexpected changes, while also trying to minimize losses. The researchers looked at how to solve this problem using an optimization method called bandit convex optimization with memory, which is different from the traditional approach used in Linear Quadratic Control (LQC). |
Keywords
* Artificial intelligence * Optimization
