Summary of An Information-theoretic Analysis Of Thompson Sampling For Logistic Bandits, by Amaury Gouverneur et al.
An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits
by Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering, Mikael Skoglund
First submitted to arxiv on: 3 Dec 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 medium-difficulty summary: This paper studies the performance of Thompson Sampling algorithm for logistic bandit problems. The algorithm receives binary rewards with probabilities determined by a logistic function and analyzes the information ratio, which quantifies the trade-off between immediate regret and gained information about optimal actions. The authors improve upon previous results by establishing that the information ratio is bounded by 9/2dalpha^-2, where alpha measures the alignment between action and parameter spaces. They then derive a bound on Bayesian expected regret of order O(d/alphasqrt(Tlog(beta*T/d))). This is the first regret bound for logistic bandits that depends only logarithmically on beta while being independent of the number of actions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The low-difficulty summary: Imagine an algorithm called Thompson Sampling that helps make decisions based on rewards. In this case, the rewards are either good or bad, and the algorithm uses a special formula to figure out what to do next. The researchers looked at how well this algorithm works when it’s trying to find the best action in a situation where the possible actions have some connection to the underlying problem. They found that the algorithm does better than expected, especially when there are many possible actions and the rewards are very close together. This is important because it helps us understand how to make good decisions in situations like this. |
Keywords
» Artificial intelligence » Alignment
