Summary of Hierarchical Upper Confidence Bounds For Constrained Online Learning, by Ali Baheri
Hierarchical Upper Confidence Bounds for Constrained Online Learning
by Ali Baheri
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 A novel framework called Hierarchical Constrained Bandits (HCB) is proposed to tackle sequential decision-making problems with hierarchical structures and multi-level constraints. The traditional Multi-Armed Bandit (MAB) problem is extended to incorporate contextual bandits, leveraging confidence bounds within a hierarchical setting. The Hierarchical Constrained Upper Confidence Bound (HC-UCB) algorithm is designed to address the complexities of the HCB problem, achieving sublinear regret bounds and high-probability guarantees for constraint satisfaction at all levels. A minimax lower bound on the regret is also derived, demonstrating the near-optimality of the proposed algorithm. This work has significant implications for real-world applications where decision-making processes are inherently hierarchical and constrained. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make decisions is introduced that takes into account when we have to make choices at different levels. This is called Hierarchical Constrained Bandits (HCB). The old way of making decisions, Multi-Armed Bandit (MAB), didn’t work well for this type of problem. A new algorithm called Hierarchical Constrained Upper Confidence Bound (HC-UCB) was created to solve the HCB problem. This algorithm does a good job of balancing trying new things and sticking with what works. It also makes sure that certain rules are followed at each level of decision-making. The results of this work could be very useful for real-world situations where decisions have to be made at multiple levels. |
Keywords
* Artificial intelligence * Probability
