Summary of Fast: An Optimization Framework For Fast Additive Segmentation in Transparent Ml, by Brian Liu and Rahul Mazumder
FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML
by Brian Liu, Rahul Mazumder
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces FAST, a novel optimization framework for fast additive segmentation. The framework is designed to produce transparent additive models by segmenting piecewise constant shape functions for each feature in a dataset. Compared to existing state-of-the-art methods like explainable boosting machines, FAST achieves optimization speeds that are approximately two orders of magnitude faster. The authors also develop new feature selection algorithms within the FAST framework to fit parsimonious models that perform well. Through experiments and case studies, the paper demonstrates how FAST improves the computational efficiency and interpretability of additive models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FAST is a new way to make computer models more transparent and efficient. It helps computers create “additive” models by breaking down data into simpler pieces. This makes it easier for people to understand what’s happening inside the model. The FAST framework is much faster than other methods, like explainable boosting machines. It also includes new ways to select which features are most important for a particular problem. The paper shows how FAST works and why it matters. |
Keywords
* Artificial intelligence * Boosting * Feature selection * Optimization
