Summary of Information Capacity Regret Bounds For Bandits with Mediator Feedback, by Khaled Eldowa et al.
Information Capacity Regret Bounds for Bandits with Mediator Feedback
by Khaled Eldowa, Nicolò Cesa-Bianchi, Alberto Maria Metelli, Marcello Restelli
First submitted to arxiv on: 15 Feb 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 The paper addresses the mediator feedback problem, a type of bandit game where the learner chooses a policy and observes an outcome with a corresponding loss. The authors introduce the concept of policy set capacity as a measure of complexity and provide new regret bounds for the EXP4 algorithm in both adversarial and stochastic settings. They also prove lower bounds for various policy sets and consider the case of varying distributions between rounds, improving upon prior results. Additionally, they show that exploiting similarities between policies is not possible under linear bandit feedback and provide a regret bound for a full-information variant. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in machine learning where you choose a strategy and then get some feedback about how well it worked. The authors use an algorithm called EXP4 to make good choices, even when the strategies are very different from each other. They also show that trying to take advantage of similarities between the strategies won’t work in this case. This is important for things like personalized advertising or recommending products. |
Keywords
* Artificial intelligence * Machine learning
