Summary of Causal Bandits: the Pareto Optimal Frontier Of Adaptivity, a Reduction to Linear Bandits, and Limitations Around Unknown Marginals, by Ziyi Liu et al.
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
by Ziyi Liu, Idan Attias, Daniel M. Roy
First submitted to arxiv on: 1 Jul 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 investigates adapting to causal structure in multi-armed bandit problems, where additional variables are observed after actions. It aims to achieve strictly better regret rates if the environment exhibits “conditionally benign” structure, while recovering worst-case regret rates if it doesn’t. The learner has no prior knowledge of whether the favorable structure holds. The paper establishes the Pareto optimal frontier of adaptive rates, proving upper and matching lower bounds on the trade-offs in conditionally benign and arbitrary environments. It also reduces the problem to linear bandits, obtaining instance-dependent bounds for causal bandits. Finally, it examines the assumption that marginal distributions are known, showing a non-trivial estimate is necessary for better-than-worst-case minimax rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers can learn from experiences in different situations. It’s trying to figure out how to get better results if the situation has certain patterns or rules. The computer doesn’t know beforehand whether these patterns are present, so it needs to adapt and adjust its learning. The paper shows that with this approach, computers can achieve better performance than usual. It also explains why knowing some details about the situation is important for getting good results. |
