# MIDA: Multiple Imputation using Denoising Autoencoders

arXiv: https://arxiv.org/abs/1705.02737 · HTML: https://mlsanity.com/paper/1705.02737/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2017.

## Built on by (most cited first, 20 of 20)

- [HyperImpute: Generalized Iterative Imputation with Automatic Model Selection](https://mlsanity.com/paper/2206.07769/) (2022)
- [MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms](https://mlsanity.com/paper/2111.03187/) (2021)
- [Transformed Distribution Matching for Missing Value Imputation](https://mlsanity.com/paper/2302.10363/) (2023)
- [Data Imputation with Iterative Graph Reconstruction](https://mlsanity.com/paper/2212.02810/) (2022)
- [Automated data processing and feature engineering for deep learning and big data applications: a survey](https://mlsanity.com/paper/2403.11395/) (2024)
- [EMFlow: Data Imputation in Latent Space via EM and Deep Flow Models](https://mlsanity.com/paper/2106.04804/) (2021)
- [TS-Diffusion: Generating Highly Complex Time Series with Diffusion Models](https://mlsanity.com/paper/2311.03303/) (2023)
- [Differentiable and Scalable Generative Adversarial Models for Data Imputation](https://mlsanity.com/paper/2201.03202/) (2022)
- [Explainability of Machine Learning Models under Missing Data](https://mlsanity.com/paper/2407.00411/) (2024)
- [MisConv: Convolutional Neural Networks for Missing Data](https://mlsanity.com/paper/2110.14010/) (2021)
- [FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Prediction](https://mlsanity.com/paper/2203.04692/) (2022)
- [Deep Learning in Single-Cell Analysis](https://mlsanity.com/paper/2210.12385/) (2022)
- [STING: Self-attention based Time-series Imputation Networks using GAN](https://mlsanity.com/paper/2209.10801/) (2022)
- [Principal Component Analysis based frameworks for efficient missing data imputation algorithms](https://mlsanity.com/paper/2205.15150/) (2022)
- [Conditional expectation with regularization for missing data imputation](https://mlsanity.com/paper/2302.00911/) (2023)
- [Blockwise Principal Component Analysis for monotone missing data imputation and dimensionality reduction](https://mlsanity.com/paper/2305.06042/) (2023)
- [Towards Better Modeling with Missing Data: A Contrastive Learning-based Visual Analytics Perspective](https://mlsanity.com/paper/2309.09744/) (2023)
- [DPER: Efficient Parameter Estimation for Randomly Missing Data](https://mlsanity.com/paper/2106.05190/) (2021)
- [Imputation using training labels and classification via label imputation](https://mlsanity.com/paper/2311.16877/) (2023)
- [Enhancing Missing Data Imputation through Combined Bipartite Graph and Complete Directed Graph](https://mlsanity.com/paper/2411.04907/) (2024)

## Abstract (arXiv)

Missing data is a significant problem impacting all domains. State-of-the-art framework for minimizing missing data bias is multiple imputation, for which the choice of an imputation model remains nontrivial. We propose a multiple imputation model based on overcomplete deep denoising autoencoders. Our proposed model is capable of handling different data types, missingness patterns, missingness proportions and distributions. Evaluation on several real life datasets show our proposed model significantly outperforms current state-of-the-art methods under varying conditions while simultaneously improving end of the line analytics.