Summary of Optimal Multi-fidelity Best-arm Identification, by Riccardo Poiani et al.
Optimal Multi-Fidelity Best-Arm Identification
by Riccardo Poiani, Rémy Degenne, Emilie Kaufmann, Alberto Maria Metelli, Marcello Restelli
First submitted to arxiv on: 5 Jun 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 This paper tackles multi-fidelity best-arm identification, where algorithms must efficiently identify the arm with the highest mean reward while balancing accuracy and cost. The authors propose a gradient-based approach that achieves asymptotically optimal cost complexity, outperforming existing methods in experiments. A tight instance-dependent lower bound on total cost is established, providing new insights for computationally efficient algorithms. Additionally, the study reveals an optimal fidelity concept for each arm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers try to solve a problem where they need to find the best option quickly while using less accurate information sometimes costs less money. The main idea is to develop an algorithm that works well and uses the right amount of information at the right time. The authors show that their new method is better than previous methods and explain why it’s important. |
