Summary of Refined Risk Bounds For Unbounded Losses Via Transductive Priors, by Jian Qian et al.
Refined Risk Bounds for Unbounded Losses via Transductive Priors
by Jian Qian, Alexander Rakhlin, Nikita Zhivotovskiy
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 In this paper, researchers revisit and generalize sequential variants of linear regression models, specifically those characterized by unbounded losses in classification problems. The key innovation lies in assuming that the set of design vectors is known in advance, allowing for transductive online learning. By leveraging the exponential weights algorithm with carefully chosen transductive priors, the authors demonstrate how to convert bounds into statistical ones without making assumptions about the distribution of design vectors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies different types of linear regression models and how they can be used for classification problems. The researchers assume that they know some information about the data beforehand, which helps them create new algorithms. These algorithms are useful because they allow the model to learn from the data without needing to make assumptions about the distribution of the data. |
Keywords
» Artificial intelligence » Classification » Linear regression » Online learning
