ArticleProceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications2021
Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems.
Article in Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation.Proceedings of machine learning research · 2025Article
- Bias and Efficiency Comparison between Multiple Imputation and Available-Case Analysis for Missing Data in Longitudinal Models.Statistics in biosciences · 2025Article
- Evaluating the risk of endometriosis based on patients' self-assessment questionnaires.Reproductive biology and endocrinology : RB&E · 2023Article
- MISNN: Multiple Imputation via Semi-parametric Neural Networks.Advances in knowledge discovery and data mining : ... Pacific-Asia Conference, PAKDD ..., proceedings. Pacific-Asia Conference on Knowledge Discovery and Data Mining · 2023Article
- Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data.Proceedings of machine learning research · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Missing data are present in most real world problems and need careful handling to preserve the prediction accuracy and statistical consistency in the downstream analysis. As the gold standard of handling missing data, multiple imputation (MI) methods are proposed to account for the imputation uncertainty and provide proper statistical inference. In this work, we propose Multiple Imputation via Generative Adversarial Network (MI-GAN), a deep learning-based (in specific, a GAN-based) multiple imputation method, that can work under missing at random (MAR) mechanism with theoretical support. MI-GAN leverages recent progress in conditional generative adversarial neural works and shows strong performance matching existing state-of-the-art imputation methods on high-dimensional datasets, in terms of imputation error. In particular, MI-GAN significantly outperforms other imputation methods in the sense of statistical inference and computational speed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.