Summary of Fast and Sample Efficient Multi-task Representation Learning in Stochastic Contextual Bandits, by Jiabin Lin et al.
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits
by Jiabin Lin, Shana Moothedath, Namrata Vaswani
First submitted to arxiv on: 2 Oct 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 In this research paper, scientists investigate how representation learning can enhance the learning efficiency of complex decision-making problems known as contextual bandit tasks. Specifically, they focus on a scenario where multiple linear bandits share common features, aiming to develop an efficient approach to solve these problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that by using a special algorithm called alternating projected gradient descent and minimization estimator, scientists can learn how to make better decisions in complex situations. The new method is tested against existing approaches and found to be effective. |
Keywords
» Artificial intelligence » Gradient descent » Representation learning
