Summary of Adaptive Regret For Bandits Made Possible: Two Queries Suffice, by Zhou Lu et al.
Adaptive Regret for Bandits Made Possible: Two Queries Suffice
by Zhou Lu, Qiuyi Zhang, Xinyi Chen, Fred Zhang, David Woodruff, Elad Hazan
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 solution to the challenge of online optimization in fast-changing states or volatile environments. The goal is to perform rapid adaptation with limited observation. The authors introduce the concept of strongly adaptive regret, which measures the maximum regret over any contiguous interval. They provide query and regret optimal bandit algorithms that achieve almost-linear regret lower bounds. Surprisingly, with just two queries per round, they present a Strongly Adaptive Bandit Learner (StABL) algorithm that achieves adaptive regret for multi-armed bandits. The authors also extend their results to the bandit convex optimization setting and empirically demonstrate the superiority of their algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us solve a big problem in online optimization. When things change quickly, we need to adapt fast but don’t have much information. The authors came up with a new way to measure how well our algorithm does called strongly adaptive regret. They also created an algorithm that uses two queries per round to make good decisions. This is important because it helps us do better in situations where things are changing quickly. |
Keywords
* Artificial intelligence * Optimization
