Summary of Towards a Framework on Tabular Synthetic Data Generation: a Minimalist Approach: Theory, Use Cases, and Limitations, by Yueyang Shen et al.
Towards a framework on tabular synthetic data generation: a minimalist approach: theory, use cases, and limitations
by Yueyang Shen, Agus Sudjianto, Arun Prakash R, Anwesha Bhattacharyya, Maorong Rao, Yaqun Wang, Joel Vaughan, Nengfeng Zhou
First submitted to arxiv on: 17 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME); 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 Medium Difficulty Summary: This paper introduces a novel approach to generating synthetic tabular data, dubbed Minimalist Synthetic Tabular Data Generation (MSTDG). The proposed framework combines an unsupervised SparsePCA encoder with an XGboost decoder, achieving state-of-the-art (SOTA) performance for structured data regression and classification tasks. The authors contrast their methodology with variational autoencoders in low-dimensional scenarios to derive necessary intuitions. They apply the MSTDG framework to high-dimensional simulated credit scoring data, parallel to real-life financial applications. Additionally, the authors demonstrate practical use cases by applying the method to robustness testing, showcasing its effectiveness in model robustness evaluation. The proposed approach guarantees interpretability and does not require extra tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper introduces a new way to create fake data that’s similar to real-world financial data. They combine two different techniques, SparsePCA and XGboost, to make this happen. This approach is better than other methods because it’s simple, easy to understand, and doesn’t need extra fine-tuning. The authors test their method on some fake credit scoring data and show that it works well for evaluating how robust a model is to different types of errors. This research has practical applications in the financial industry. |
Keywords
» Artificial intelligence » Classification » Decoder » Encoder » Fine tuning » Regression » Unsupervised » Xgboost
