Summary of Non-stochastic Bandits with Evolving Observations, by Yogev Bar-on and Yishay Mansour
Non-stochastic Bandits With Evolving Observations
by Yogev Bar-On, Yishay Mansour
First submitted to arxiv on: 27 May 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 This novel online learning framework unifies and generalizes pre-established models for handling delayed and corrupted feedback in adversarial environments where action feedback evolves over time. The proposed regret minimization algorithms for full-information and bandit settings quantify regret bounds by average feedback accuracy relative to true loss, matching known bounds across many special cases while introducing new ones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way of learning online that can handle tricky situations where the feedback you get isn’t always accurate. It’s like playing a game where the rules change every round and you have to adapt quickly. The authors came up with algorithms that can work in different situations, like when you know what you’re doing or when you’re just guessing. They even figured out how well these algorithms perform compared to others. |
Keywords
* Artificial intelligence * Online learning
