Summary of Local: Learning with Orientation Matrix to Infer Causal Structure From Time Series Data, by Jiajun Zhang et al.
LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Data
by Jiajun Zhang, Boyang Qiang, Xiaoyu Guo, Weiwei Xing, Yue Cheng, Witold Pedrycz
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel approach to discovering Directed Acyclic Graphs (DAGs) from time series data is proposed, addressing scalability challenges in existing methods. The Local algorithm (LOCAL) optimizes a quasi-maximum likelihood score function, incorporating two adaptive modules: Asymptotic Causal Mask Learning (ACML) and Dynamic Graph Parameter Learning (DGPL). ACML uses learnable priority vectors and the Gumbel-Sigmoid function to construct causal masks, while DGPL transforms causal learning into decomposed matrix products. Experimental results on synthetic and real-world datasets show that LOCAL outperforms existing methods, demonstrating its potential for robust and efficient dynamic causal discovery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LOCAL is a new way to find the underlying structure in time series data. It’s hard because the relationships between variables are complex and change over time. Existing methods try to find the best solution by optimizing a score, but they get slower as the number of variables increases. LOCAL solves this problem by using two special modules: one learns how to prioritize which variables are important, and another breaks down the learning process into smaller pieces that can be solved efficiently. This makes it better at handling big datasets and easier to understand. |
Keywords
» Artificial intelligence » Likelihood » Mask » Sigmoid » Time series
