mlsanityData to 14 Oct 2026

Gaussian Processes for Missing Value Imputation

arXiv 2204.04648 ↗First posted 10 Apr 2022stat.ML cs.LG stat.APcited by 5

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Abstract (arXiv)

Missing values are common in many real-life datasets. However, most of the current machine learning methods can not handle missing values. This means that they should be imputed beforehand. Gaussian Processes (GPs) are non-parametric models with accurate uncertainty estimates that combined with sparse approximations and stochastic variational inference scale to large data sets. Sparse GPs can be used to compute a predictive distribution for missing data. Here, we present a hierarchical composition of sparse GPs that is used to predict missing values at each dimension using all the variables from the other dimensions. We call the approach missing GP (MGP). MGP can be trained simultaneously to impute all observed missing values. Specifically, it outputs a predictive distribution for each missing value that is then used in the imputation of other missing values. We evaluate MGP in one private clinical data set and four UCI datasets with a different percentage of missing values. We compare the performance of MGP with other state-of-the-art methods for imputing missing values, including variants based on sparse GPs and deep GPs. The results obtained show a significantly better performance of MGP.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 15 of its 34 references that are in the map. First posted on arXiv 10 Apr 2022.

Builds on

15 of 34 references.

References · 15

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015Variational Dropout and the Local Reparameterization Trickcited by 490
  3. 2013Gaussian Processes for Big Datacited by 383
  4. 2018GAIN: Missing Data Imputation using Generative Adversarial Netscited by 352
  5. 2014Scalable Variational Gaussian Process Classificationcited by 228
  6. 2019GP-VAE: Deep Probabilistic Time Series Imputationcited by 172
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  8. 2017Doubly Stochastic Variational Inference for Deep Gaussian Processescited by 143
  9. 2015MCMC for Variationally Sparse Gaussian Processescited by 44
  10. 2016Deep Gaussian Processes for Regression using Approximate Expectation Propagationcited by 39
  11. 2021Computational Graph Completioncited by 28
  12. 2020Multi-class Gaussian Process Classification with Noisy Inputscited by 10
3 more
  1. 2015Recurrent Gaussian Processescited by 9
  2. 2016Filling the gaps: Gaussian mixture models from noisy, truncated or incomplete samplescited by 5
  3. 2017Scalable Multi-Class Gaussian Process Classification using Expectation Propagationcited by 5

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 3.

5 papers.

  1. 2023RedCore: Relative Advantage Aware Cross-modal Representation Learning for Missing Modalities with Imbalanced Missing Ratescited by 15
  2. 2024Explainability of Machine Learning Models under Missing Datacited by 5
  3. 2025JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMscited by 4
  4. 2025Integrative Analysis and Imputation of Multiple Data Streams via Deep Gaussian Processescited by 0
  5. 2025IBMA: An Imputation-Based Mixup Augmentation Using Self-Supervised Learning for Time Series Datacited by 0