Summary of Local Anti-concentration Class: Logarithmic Regret For Greedy Linear Contextual Bandit, by Seok-jin Kim et al.
Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit
by Seok-Jin Kim, Min-hwan Oh
First submitted to arxiv on: 19 Nov 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 The paper explores the performance guarantees of exploration-free greedy algorithms for the linear contextual bandit problem. It introduces a novel condition called Local Anti-Concentration (LAC), which enables greedy bandit algorithms to achieve provable efficiency. The LAC condition is satisfied by a broad class of distributions, including Gaussian, exponential, uniform, Cauchy, and Student’s t distributions, expanding the range of efficient algorithm performance. Under the proposed LAC condition, the paper proves that the cumulative expected regret of the greedy algorithm for the linear contextual bandit is bounded by O(poly log T). This establishes the widest range of distributions known to date that allow a sublinear regret bound for greedy algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how good some computer programs are at making decisions. These programs don’t need to try different options first, they just pick one and see how it goes. The researchers found a way to make these programs work better by introducing a new condition called Local Anti-Concentration (LAC). This condition helps the programs decide what to do based on some rules. They showed that many types of data distributions can be used with this program, making it very useful. |
