Summary of Improved Bound For Robust Causal Bandits with Linear Models, by Zirui Yan et al.
Improved Bound for Robust Causal Bandits with Linear Models
by Zirui Yan, Arpan Mukherjee, Burak Varıcı, Ali Tajer
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 paper investigates the robustness of causal bandits (CBs) in the face of temporal model fluctuations. The authors consider linear structural equation models (SEMs) with unknown and time-varying causal relationships, assuming no prior knowledge of these changes. A sequence of interventions is designed to minimize cumulative regret compared to an oracle aware of the entire causal model and its fluctuations. The proposed robust CB algorithm achieves upper bounds on regret, showing that it can maintain sub-linear regret for a broad range of unknown model deviations. This work has implications for applications where models are subject to temporal changes, such as recommender systems or clinical trials. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machines learn from experiences and adapt to changing patterns. The researchers studied a special kind of learning called causal bandits, which involves making decisions based on incomplete information. They explored what happens when the rules that govern these decisions change over time. To make better choices, they developed an algorithm that can learn from experience and adjust to new situations. This work could help improve decision-making in fields like medicine or online recommendations. |
Keywords
» Artificial intelligence » Temporal model
