Summary of High-dimensional Differential Parameter Inference in Exponential Family Using Time Score Matching, by Daniel J. Williams et al.
High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching
by Daniel J. Williams, Leyang Wang, Qizhen Ying, Song Liu, Mladen Kolar
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 tackles differential inference in time-varying parametric probabilistic models, specifically graphical models with changing structures. Instead of estimating a high-dimensional model at each time point and then inferring changes later, the authors directly learn the differential parameter, which is the time derivative of the parameter. They treat the time score function of an exponential family model as a linear model of the differential parameter for direct estimation using time score matching to estimate parameter derivatives. The paper also establishes the consistency of a regularized score matching objective and demonstrates the finite-sample normality of a debiased estimator in high-dimensional settings. The authors verify their methodology’s effectiveness on simulated and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how things change over time, especially in complex systems like social networks or brain activity. Instead of trying to figure out all the details at once, it focuses on understanding how these systems evolve. It does this by looking at “time scores” – a way to measure how much something changes – and using that information to learn more about what’s happening over time. The authors test their ideas on fake data and real data from social networks, and they work well. |
Keywords
» Artificial intelligence » Inference
