Summary of Batched Online Contextual Sparse Bandits with Sequential Inclusion Of Features, by Rowan Swiers et al.
Batched Online Contextual Sparse Bandits with Sequential Inclusion of Features
by Rowan Swiers, Subash Prabanantham, Andrew Maher
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 Multi-armed Bandits (MABs) are widely applied in online platforms and e-commerce for personalized user experience optimization. This work focuses on the Contextual Bandit problem with linear rewards under sparsity and batched data conditions. A novel algorithm, Online Batched Sequential Inclusion (OBSI), is proposed to ensure fairness by excluding irrelevant features from decision-making processes based on their confidence impact on rewards. OBSI sequentially includes features as confidence increases. Experimental results on synthetic data demonstrate the superiority of OBSI over other algorithms in terms of regret, feature relevance, and computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a situation where you have to make many decisions for lots of people online. This paper is about making those decisions better by using special math called Multi-armed Bandits (MABs). They help with personalized experiences. The problem is that sometimes some information isn’t useful, and we want to ignore it. A new way to do this was created, called Online Batched Sequential Inclusion (OBSI). It looks at the importance of each piece of information and only uses the important ones. The results show that OBSI works better than other methods in making good decisions. |
Keywords
» Artificial intelligence » Optimization » Synthetic data
