Summary of Noise-adaptive Confidence Sets For Linear Bandits and Application to Bayesian Optimization, by Kwang-sung Jun et al.
Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization
by Kwang-Sung Jun, Jungtaek Kim
First submitted to arxiv on: 12 Feb 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 This paper tackles the challenging problem of adapting to unknown noise levels in sequential decision-making, particularly in linear bandits. The authors propose two novel confidence sets that improve upon prior work by Abbasi-Yadkori et al. (2011). The first confidence set is semi-adaptive, scaling with the square root of the dimension and unknown sub-Gaussian parameter, leading to an improved regret bound. The second confidence set is variance-adaptive, showing better numerical performance than existing methods. The authors develop a practical variance-adaptive linear bandit algorithm using an optimistic approach and provide empirical evaluation in Bayesian optimization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in making good decisions one after another. Usually, you need to know how much noise is involved, but what if that’s unknown? The researchers found two new ways to deal with this uncertainty: semi-adaptive confidence sets and variance-adaptive confidence sets. These new ideas help reduce mistakes made while exploring and improve the overall performance of decision-making algorithms. |
Keywords
* Artificial intelligence * Optimization
