Summary of Cost Aware Best Arm Identification, by Kellen Kanarios et al.
Cost Aware Best Arm Identification
by Kellen Kanarios, Qining Zhang, Lei Ying
First submitted to arxiv on: 26 Feb 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 A best arm identification problem is studied with dual objects, where each arm has both rewards and costs. The goal is to identify the largest reward arm using the minimum expected cost, which captures the separation between testing and implementation phases in product development pipelines. The paper derives a theoretical lower bound and proposes two algorithms: CTAS, which matches the lower bound asymptotically, and Chernoff Overlap (CO), based on a square-root rule, which is optimal in simplified models and generalizes well numerically. Results show that ignoring heterogeneous action costs leads to sub-optimality and simple algorithms can achieve near-optimal performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to choose the best option when it has both good things (rewards) and bad things (costs). It’s like deciding which product to develop, considering both its potential rewards and development costs. The researchers came up with a way to solve this problem and found that ignoring these costs can lead to making the wrong choice. They also showed that simple algorithms can be very effective in finding the best option. |
