Summary of Handling Delayed Feedback in Distributed Online Optimization : a Projection-free Approach, by Tuan-anh Nguyen et al.
Handling Delayed Feedback in Distributed Online Optimization : A Projection-Free Approach
by Tuan-Anh Nguyen, Nguyen Kim Thang, Denis Trystram
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS)
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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 The proposed study investigates online convex optimization (OCO) under adversarial delayed feedback, a crucial problem for learning at the edges. The goal is to design simple, robust, and reliable algorithms that can execute locally and perform well in distributed settings with network issues. Two projection-free algorithms are presented, one for centralized and another for distributed settings, both achieving an optimal regret bound of O(sqrt(B)) where B is the sum of delay. Theoretical analyses and experimental evaluations on real-world problems demonstrate the effectiveness of these novel approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have a big problem to solve: making sure machines can learn from data even when there’s a delay in getting that data. This is important for things like self-driving cars, where the car needs to make decisions quickly based on information it receives from sensors. The study proposes two new algorithms that can handle these delayed feedback situations and still perform well. These algorithms are simple, robust, and reliable, making them useful for real-world applications. |
Keywords
* Artificial intelligence * Optimization
