Summary of Lasso Bandit with Compatibility Condition on Optimal Arm, by Harin Lee et al.
Lasso Bandit with Compatibility Condition on Optimal Arm
by Harin Lee, Taehyun Hwang, Min-hwan Oh
First submitted to arxiv on: 2 Jun 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 In this paper, researchers tackle a type of stochastic linear bandit problem where only a subset of context features affects the reward function. They propose an algorithm that adapts the forced-sampling technique and prove it achieves regret bounds logarithmic in the ambient dimension d, under the margin condition. This is achieved without additional diversity assumptions, making it strictly weaker than existing Lasso bandit literature. The proposed algorithm requires the weakest assumptions among Lasso bandit algorithms achieving O(poly log dT) regret. Experiments confirm its superior performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies a type of problem where you want to make good choices based on limited information. They show that by using a special technique, you can make better decisions without needing extra information about the situation. This is useful because it makes the algorithm more powerful and easier to use. The researchers test their idea and find that it really works well. |
