Summary of Linear Bandits with Polylogarithmic Minimax Regret, by Josep Lumbreras et al.
Linear bandits with polylogarithmic minimax regret
by Josep Lumbreras, Marco Tomamichel
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 In this research paper, a novel algorithm is proposed for linear stochastic bandits with subgaussian noise. The algorithm exhibits a minimax regret scaling of log^3(T) in the time horizon T, which is significantly faster than typical bandit algorithms that scale at sqrt(T). This is achieved through weighted least-squares estimation and geometrical arguments that are independent of the noise model. The expected regret in each time step is tightly controlled to be O(1/t), leading to a cumulative regret scaling of log^3(T). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies a new way to solve a type of problem called linear stochastic bandits. It’s like trying to find the best arm in a row of slot machines, but with some noise that makes it hard to know which one is working the best. The researchers come up with an algorithm that can figure this out faster than usual methods. They use special math tricks to make sure their algorithm doesn’t get too confused by the noise. |
