Summary of Linear Contextual Bandits with Interference, by Yang Xu et al.
Linear Contextual Bandits with Interference
by Yang Xu, Wenbin Lu, Rui Song
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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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 introduces a systematic framework to address interference in Linear Contextual Bandits (LinCB), bridging the gap between causal inference and online decision-making. It proposes algorithms that quantify the interference effect in the reward modeling process, providing theoretical guarantees such as sublinear regret bounds, finite sample upper bounds, and asymptotic properties. The approach is demonstrated through simulations and a synthetic data generated based on MovieLens data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to make better decisions when multiple agents interact with each other. In situations where many people or machines are making choices together, interference can occur, which affects the estimation of expected rewards for different options. The authors develop a new framework that takes into account this interference and provides guarantees about its performance. This could be useful in areas like personalized recommendations or resource allocation. |
Keywords
» Artificial intelligence » Inference » Synthetic data
