Summary of Sparsity-agnostic Linear Bandits with Adaptive Adversaries, by Tianyuan Jin et al.
Sparsity-Agnostic Linear Bandits with Adaptive Adversaries
by Tianyuan Jin, Kyoungseok Jang, Nicolò Cesa-Bianchi
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper studies stochastic linear bandits, where a learner chooses an action from a set of feature vectors and receives a stochastic reward. The expected reward is a fixed but unknown linear function of the chosen action. The paper focuses on sparse regret bounds that depend on the number S of non-zero coefficients in the linear reward function. Unlike previous works that assumed knowledge of S or additional assumptions about the action sets, this work obtains sparse regret bounds when S is unknown and the action sets are adversarially generated. The techniques used combine online-to-confidence-set conversions with a novel randomized model selection approach over a hierarchy of nested confidence sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers study a type of machine learning problem called stochastic linear bandits. Imagine you’re trying to figure out what’s the best way to get a reward in a situation where some actions might be better than others, but you don’t know exactly how they’ll work out. This is kind of like trying to decide which restaurant to go to based on reviews and menus, without knowing for sure which one will give you the most enjoyable experience. The paper shows how to do this efficiently by using a combination of techniques that help you figure out what’s working well and what’s not. |
Keywords
» Artificial intelligence » Machine learning
