Loading Now

Summary of Masked Language Modeling Becomes Conditional Density Estimation For Tabular Data Synthesis, by Seunghwan An et al.


Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis

by Seunghwan An, Gyeongdong Woo, Jaesung Lim, ChangHyun Kim, Sungchul Hong, Jong-June Jeon

First submitted to arxiv on: 31 May 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Computation and Language (cs.CL)

     Abstract of paper      PDF of paper


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
The proposed MaCoDE method generates synthetic data for heterogeneous tabular datasets with high machine learning utility (MLu) by redefining the consecutive multi-class classification task of Masked Language Modeling (MLM) as histogram-based non-parametric conditional density estimation. This approach enables estimating conditional densities across arbitrary combinations of target and conditional variables, bridging the theoretical gap between distributional learning and MLM. The MaCoDE model is evaluated on 10 real-world datasets, demonstrating its ability to adjust data privacy levels easily without re-training. Furthermore, it shows effectiveness in handling training datasets with missing values, including multiple imputations.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper creates a new way to make synthetic data for mixed-type datasets that machines can learn from well. They do this by changing the task of language models into one that estimates conditional densities. This helps them create data that’s realistic and useful for machine learning. The method, called MaCoDE, is tested on 10 real-world datasets and shows it can adjust privacy levels without re-training. It also works well with missing values in the training data.

Keywords

» Artificial intelligence  » Classification  » Density estimation  » Machine learning  » Synthetic data