Summary of Multi-armed Bandits with Missing Outcome, by Ilia Mahrooghi et al.
Multi-armed Bandits with Missing Outcome
by Ilia Mahrooghi, Mahshad Moradi, Sina Akbari, Negar Kiyavash
First submitted to arxiv on: 8 Nov 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 research paper proposes a novel methodology to handle missing outcomes in multi-armed bandits (MAB), a crucial problem in online decision-making. The existing algorithms often overlook this challenge or assume random missingness, leading to biased estimates and linear regret. To address this gap, the authors analyze the impact of different missingness mechanisms on achievable regret bounds and introduce algorithms that account for missingness under both missing at random (MAR) and missing not at random (MNAR) models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better decisions in uncertain situations by considering when we don’t know the outcome. Imagine you’re trying to decide which medicine to take based on how others have done, but some people didn’t report their results. This can lead to bad choices if we don’t account for those missing outcomes. The researchers found a way to improve decision-making by taking into account different types of missingness and developed algorithms that work better than before. |
