Summary of Towards Efficient and Optimal Covariance-adaptive Algorithms For Combinatorial Semi-bandits, by Julien Zhou (thoth et al.
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits
by Julien Zhou, Pierre Gaillard, Thibaud Rahier, Houssam Zenati, Julyan Arbel
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST); 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 The paper addresses the problem of stochastic combinatorial semi-bandits, where a player selects among P actions from the power set of a set containing d base items. To achieve optimal regret upper bounds, adaptivity to the problem’s structure is crucial. The authors design “optimistic” covariance-adaptive algorithms, OLS-UCB-C and COS-V, that rely on online estimations of the covariance structure. These algorithms yield improved gap-free regret, with COS-V being the first sampling-based algorithm satisfying a T^1/2 gap-free regret (up to poly-logs). The approach efficiently leverages semi-bandit feedback and outperforms bandit feedback approaches in some cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at a special kind of problem where we have to make decisions from a really big set of possibilities. To do this well, we need to adapt to the structure of the problem. The authors come up with new ways to solve this problem by using information about how different things relate to each other. These new methods work better than old ones and can even be used in cases where there are more options than before. |
