Summary of Online Sequential Decision-making with Unknown Delays, by Ping Wu and Heyan Huang and Zhengyang Liu
Online Sequential Decision-Making with Unknown Delays
by Ping Wu, Heyan Huang, Zhengyang Liu
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework for online sequential decision-making in situations where feedback is delayed and uncertain. It addresses the limitations of previous research by introducing three families of algorithms that can handle different types of received feedback, including full information, gradient information, and value information. The proposed algorithms are versatile and applicable to universal norms, offering improved performance over existing methods. Regret bounds are provided for each algorithm under various assumptions, demonstrating their efficiency in different scenarios. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us make better decisions when we don’t get the results right away. Imagine you’re playing a game where you need to learn from your mistakes, but it takes some time to see how you did. The authors came up with new ways to play this game that work well even when the feedback is delayed or incomplete. They tested these methods and showed they can make better choices than before. This is important because it helps us solve problems in many areas, like finance, healthcare, and more. |