Summary of Optimal Best-arm Identification in Linear Bandits, by Yassir Jedra et al.
Optimal Best-arm Identification in Linear Bandits
by Yassir Jedra, Alexandre Proutiere
First submitted to arxiv on: 29 Jun 2020
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 The proposed algorithm for identifying the best arm in stochastic linear bandits achieves fixed confidence while minimizing sampling budget, matching instance-specific lower bounds asymptotically almost surely and in expectation. The algorithm tracks an optimal proportion of arm draws, which can be updated infrequently without compromising guarantees. Unlike existing strategies, it uses a stopping rule independent of the number of arms. Experimental results show significant performance improvements over existing algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem called best-arm identification in something called stochastic linear bandits. It’s like trying to find the best option from many choices while being sure you’re right and not wasting too much time or effort. The new algorithm is special because it can adjust its guesses about which option is best without getting worse results, and it doesn’t care how many options there are. In tests, this algorithm worked better than other approaches. |
