Summary of Gamformer: In-context Learning For Generalized Additive Models, by Andreas Mueller et al.
GAMformer: In-Context Learning for Generalized Additive Models
by Andreas Mueller, Julien Siems, Harsha Nori, David Salinas, Arber Zela, Rich Caruana, Frank Hutter
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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 Generalized Additive Models (GAMs) are widely used for creating fully interpretable machine learning models for tabular data. This paper introduces GAMformer, a novel method that leverages in-context learning to estimate shape functions of a GAM in a single forward pass, departing from traditional iterative approaches. Building on previous research applying in-context learning to tabular data, the authors train GAMformer using complex synthetic data and demonstrate its ability to extrapolate well to real-world data. The experiments show that GAMformer performs competitively with other leading GAMs across various classification benchmarks while generating highly interpretable shape functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to create special kinds of machine learning models called Generalized Additive Models (GAMs). Instead of using old methods, this new method is called GAMformer and it can learn in just one step. The researchers trained GAMformer using fake data and then tested it on real data. They found that GAMformer worked well and was able to create easy-to-understand models that are as good as other popular models. |
Keywords
» Artificial intelligence » Classification » Machine learning » Synthetic data
