Summary of Identification Of Non-causal Graphical Models, by Junyao You et al.
Identification of Non-causal Graphical Models
by Junyao You, Mattia Zorzi
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC)
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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 This paper proposes a novel approach to estimating non-causal graphical models that encode smoothing relations among variables. The authors introduce a new covariance extension problem and show that the solution minimizing transportation distance with respect to white noise process is a double-sided autoregressive non-causal graphical model. They also generalize this paradigm to a class of graphical autoregressive moving-average models. Numerical experiments demonstrate the effectiveness of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a tricky problem in machine learning by creating new types of graphs that can be used for smoothing variables. The authors show how to minimize the difference between these graphs and ones made from random noise, which is useful for understanding complex systems. They also test their idea on some examples and it works well. |
Keywords
» Artificial intelligence » Autoregressive » Machine learning
