Summary of Adaptive Smooth Non-stationary Bandits, by Joe Suk
Adaptive Smooth Non-Stationary Bandits
by Joe Suk
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 tackles a K-armed non-stationary bandit model where rewards change smoothly over time. The study assumes Hölder class assumptions on rewards as functions of time, which are parametrized by a Hölder exponent and coefficient . The authors first establish the minimax dynamic regret rate for all values of K, , and , then show that this optimal dynamic regret can be achieved adaptively without knowledge of these parameters. This resolves open questions in the literature, which previously only addressed limited regimes of and . The paper’s contributions have implications for understanding dynamic regret rates in non-stationary bandit models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about a special kind of problem where machines learn to make decisions over time. It’s called a “non-stationary bandit” because the rewards (or payoffs) change smoothly as time goes on. The researchers looked at how to find the best way to make these decisions without knowing exactly when or why the rewards will change. They found that there is an optimal way to do this, and they showed that it’s possible to achieve this without needing to know all the details about how the rewards are changing. |
