Summary of Under the Hood Of Tabular Data Generation Models: Benchmarks with Extensive Tuning, by G. Charbel N. Kindji (lacodam) et al.
Under the Hood of Tabular Data Generation Models: Benchmarks with Extensive Tuning
by G. Charbel N. Kindji, Lina Maria Rojas-Barahona, Elisa Fromont, Tanguy Urvoy
First submitted to arxiv on: 18 Jun 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 This study addresses the need for a unified evaluation of generative models for tabular data generation by benchmarking five recent model families on 16 datasets with varying characteristics. The researchers optimize hyperparameters, feature encodings, and architectures to investigate the impact of dataset-specific tuning on performance. They find that large-scale tuning substantially improves performance compared to original configurations, with diffusion-based models generally outperforming others on tabular data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative models can create realistic and useful table data, but it’s not easy because tables are complex. The study looks at five types of models that can make tables and tests them on 16 different datasets. They want to see if the models work better when they’re trained just for each dataset or if they do okay with general settings. They found out that most models need special training for each dataset to do their best, and some models are really good at making tables. |
Keywords
* Artificial intelligence * Diffusion
