Summary of Optimal Cross-learning For Contextual Bandits with Unknown Context Distributions, by Jon Schneider et al.
Optimal cross-learning for contextual bandits with unknown context distributions
by Jon Schneider, Julian Zimmert
First submitted to arxiv on: 3 Jan 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 proposes contextual bandit algorithms in the “cross-learning” setting, where the learner observes losses for all possible contexts, not just the current round. The focus is on designing efficient algorithms that achieve nearly tight regret bounds. Specifically, the authors resolve an open problem by providing an algorithm with a regret bound of (), independent of the number of contexts. This leads to improved regret bounds for learning to bid in first-price auctions and sleeping bandits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a tricky problem in machine learning called “cross-learning” contextual bandits. It’s like trying to make good decisions when you have information about all possible situations, not just the one you’re in right now. The authors came up with an efficient way to make these decisions that does really well. This has big implications for things like online auctions and games where you don’t know what the other players will do. |
Keywords
* Artificial intelligence * Machine learning
