Summary of Minimum Empirical Divergence For Sub-gaussian Linear Bandits, by Kapilan Balagopalan and Kwang-sung Jun
Minimum Empirical Divergence for Sub-Gaussian Linear Bandits
by Kapilan Balagopalan, Kwang-Sung Jun
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel linear bandit algorithm called LinMED (Linear Minimum Empirical Divergence) is proposed, which extends the MED algorithm for multi-armed bandits. LinMED is a randomized algorithm that computes arm sampling probabilities using a closed-form formula, unlike linear Thompson sampling. This feature is useful for off-policy evaluation, where unbiased evaluation requires accurate computation of sampling probability. The algorithm enjoys near-optimal regret bounds and outperforms state-of-the-art algorithms in empirical studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LinMED is a new way to make decisions when you don’t know which choice will be best. It’s like a special kind of coin flip, but it chooses the option that is most likely to work well. This algorithm is helpful because it lets us test how well something works without actually using it. LinMED is better than other methods at making these kinds of tests, and it does a good job in real-world situations. |
Keywords
» Artificial intelligence » Probability
