Summary of Learning Generalization and Regularization Of Nonhomogeneous Temporal Poisson Processes, by Son Nguyen Van and Hoai Nguyen Xuan
Learning Generalization and Regularization of Nonhomogeneous Temporal Poisson Processes
by Son Nguyen Van, Hoai Nguyen Xuan
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The proposed paper tackles a crucial issue in the estimation of Non-Homogeneous Poisson Processes (NHPPs) with finite and limited data, which is typically addressed using non-data-driven binning methods. The authors mathematically demonstrate that these methods can lead to overfitting when dealing with limited data. To mitigate this problem, they introduce a framework for regularized learning of NHPPs, incorporating two novel adaptive and data-driven binning methods that eliminate the need for ad-hoc tuning of parameters. Experimental results on synthetic and real-world datasets showcase the effectiveness of these innovative approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to count things in a smart way when we don’t have all the information. Right now, most experts use a method called binning to estimate Non-Homogeneous Poisson Processes (NHPPs), but this can be tricky if we only have a little data. The authors show that binning methods can actually cause problems when we’re dealing with limited data. To fix this, they came up with two new ways of grouping data that work well even when we don’t have much information. They tested these methods on made-up and real-world data, and it looks like they really make a difference. |
Keywords
* Artificial intelligence * Overfitting
