Summary of Symmetric Linear Bandits with Hidden Symmetry, by Nam Phuong Tran et al.
Symmetric Linear Bandits with Hidden Symmetry
by Nam Phuong Tran, Anh Ta, Debmalya Mandal, Long Tran-Thanh
First submitted to arxiv on: 22 May 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 investigates high-dimensional linear bandits with low-dimensional structure, specifically focusing on symmetry. Unlike sparsity, symmetry is another common inductive bias that assumes the reward is invariant under certain transformations on the set of arms. The authors study online learning scenarios where the learner must identify the correct hidden symmetry without knowing it beforehand. They propose a method based on model selection within collections of low-dimensional subspaces and demonstrate its effectiveness with regret bounds of O(d_0{2/3}T{2/3}log(d)) or O(d_0√Tlog(d)) depending on well-separated models. Their algorithm can handle large ambient dimensions, making it relevant to real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a type of machine learning problem called high-dimensional linear bandits. It’s about finding the best solution when you have many options and only some of them are good. The researchers look at a specific kind of symmetry that can help us make better decisions. They develop an algorithm to learn this symmetry online, without knowing it beforehand. Their method is efficient and can handle large amounts of data, making it useful for real-world applications. |
Keywords
» Artificial intelligence » Machine learning » Online learning
