Summary of A Unified Confidence Sequence For Generalized Linear Models, with Applications to Bandits, by Junghyun Lee et al.
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
by Junghyun Lee, Se-Young Yun, Kwang-Sung Jun
First submitted to arxiv on: 19 Jul 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 presents a unified likelihood ratio-based confidence sequence (CS) for generalized linear models (GLMs), which is guaranteed to be convex and numerically tight. The CS is shown to be on par or improve upon known CSs for various GLMs, including Gaussian, Bernoulli, and Poisson. The paper also proposes an optimistic algorithm called OFUGLB, applicable to any generalized linear bandits (GLBs). The analysis shows that the algorithm simultaneously attains state-of-the-art regrets for various self-concordant GLBs, and even poly(S)-free for bounded GLBs, including logistic bandits. The paper’s technical novelties include a time-uniform PAC-Bayesian bound with a uniform prior/posterior, and a new proof technique that avoids the self-concordant control lemma. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For curious learners or general audiences, this paper is about developing a new way to measure how confident we are in our predictions when using complex mathematical models. These models help us make decisions based on data, but they can be tricky to work with because they can produce different results depending on the specific data used. The researchers created a new method that helps ensure their predictions are accurate and reliable, even when working with different types of data. This is important for many real-world applications, such as medical research or financial modeling. |
Keywords
» Artificial intelligence » Likelihood
