Summary of Learning the Topology and Behavior Of Discrete Dynamical Systems, by Zirou Qiu et al.
Learning the Topology and Behavior of Discrete Dynamical Systems
by Zirou Qiu, Abhijin Adiga, Madhav V. Marathe, S. S. Ravi, Daniel J. Rosenkrantz, Richard E. Stearns, Anil Vullikanti
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper, researchers tackle the challenge of learning both the behavior and underlying topology of a black-box system, which is computationally intractable in general. However, they develop efficient learning methods under the PAC model when the graph belongs to certain classes. Additionally, they propose a relaxed setting where the topology is partially observed and establish the sample complexity for an efficient PAC learner. The paper also provides theoretical foundations for learning both behavior and topology of discrete dynamical systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to learn about the spread of diseases on networks. Normally, we assume we know the network structure, but in reality, it can be hard to figure out. Researchers show that this problem is really hard, but they also find some cases where it’s easier to solve. They even develop a new way to do it when we don’t have complete information about the network. This work helps us understand how to learn more about complex systems like disease spread. |
