Summary of High Probability Bound For Cross-learning Contextual Bandits with Unknown Context Distributions, by Ruiyuan Huang et al.
High Probability Bound for Cross-Learning Contextual Bandits with Unknown Context Distributions
by Ruiyuan Huang, Zengfeng Huang
First submitted to arxiv on: 5 Oct 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 In this paper, researchers investigate contextual bandits with cross learning, a problem that arises when observing losses across all possible contexts rather than just the current round’s context. The focus is on a setting where losses are chosen adversarially and contexts are sampled independently from a specific distribution. Building upon previous work by Balseiro et al. (2019) and Schneider and Zimmert (2023), this paper presents a novel analysis of the algorithm proposed by Schneider and Zimmert, demonstrating that it achieves near-optimal regret with high probability. The authors introduce new insights by leveraging the weak dependency structure between epochs, which was previously overlooked. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machines can make good decisions when faced with many options and unclear information. It’s about a special kind of problem where the machine sees what happens after making each choice, but it doesn’t know which choice is best upfront. The researchers want to find an algorithm that helps the machine make great choices even if other people or factors are trying to trick it. They’re building on previous work and adding new ideas to show that their approach works really well. |
Keywords
» Artificial intelligence » Probability
