New ANN method for imputing rounded zeros in compositional data.
problem Imputing missing values in compositional data with rounded zeros.
method Artificial Neural Networks (ANNs) for imputation of compositional data.
result ANNs are competitive or better than conventional methods for imputing rounded zeros.
Zero imputation leads to suboptimal neural network performance.
problem Suboptimal performance in neural networks due to missing data.
method Introduced Sparsity Normalization (SN) to handle missingness.
result Debiasing input-level sparsity improves neural network performance and stability.
NeuralPrefix fills in missing sensor data without additional training.
problem Data intermittency in real-world sensing.
method NeuralPrefix is a task-agnostic, zero-shot imputation framework.
result NeuralPrefix accurately recovers missing samples and generalizes to unseen datasets.
CFMI improves missing data imputation across various data types and dimensions.
problem Imputing missing data in complex, high-dimensional datasets.
method Combines normalising flows, flow-matching, and shared conditional modelling.
result Outperforms traditional and modern imputation methods across multiple metrics.
Proposes a multi-view VAE for imputing missing data from correlated sources.
problem Imputing missing data from multi-view sources with latent space correlation.
method Enforces a joint prior with latent space correlation between VAEs trained on each view.
result More strongly correlated latent spaces are uncovered, enabling effective imputation.
Random imputation is surprisingly effective for linear predictors in missing data scenarios.
problem The effectiveness of naive imputation in missing data scenarios for linear predictors.
method A unique random features model framework to study predictive performances.
result Naive imputation is negligible in bias for linear predictors under MCAR assumption.
Latent diffusion improves robustness in missing data imputation.
problem Missing data imputation under MCAR corruption.
method Two-stage framework: VAE for latent feature learning, diffusion model in latent space.
result Latent diffusion maintains high quality and stability up to 50% missingness.
A new model for data with zeros or missing values.
problem Data with excess zeros or missing values.
method Composite loss framework for low-rank modeling, combining generalized low-rank and hurdle methods.
result Demonstrated on a manufacturing data set and applied to missing value imputation.
Study compares tree-based imputation methods to MICE PMM for missing data.
problem Missing data in empirical studies.
method Various imputation methods including MICE PMM, RF, missRanger, and MIXGBoost.
result Tree-based imputations, especially RF and missRanger with PMM, perform better.
A comprehensive benchmark of 15 scRNA-seq imputation methods across various datasets and analyses.
problem Imputation of single-cell RNA sequencing data to recover latent transcriptional signals.
method Evaluation of 15 imputation methods across 30 datasets and 6 downstream analyses.
result Traditional methods generally outperform DL-based methods in scRNA-seq data analysis.
Improved surname geocoding and name supplements enhance race imputation accuracy.
problem Census data problems affecting race imputation accuracy.
method Fully Bayesian Improved Surname Geocoding (fBISG) and name supplements.
result Significant improvement in race imputation accuracy across all racial groups.
A new imputation method MissARF uses adversarial random forests for fast and accurate missing value imputation.
problem Handling missing values in biostatistical analyses.
method Adversarial Random Forests (ARF) for density estimation and data synthesis.
result MissARF performs comparably to state-of-the-art methods in imputation quality and runtime.
Proposes CBMI for missing data imputation using labels and input.
problem Missing data in practical data science settings.
method CBMI: imputes labels and input simultaneously; IUL: stacks label into input.
result CBMI improves classification accuracy, especially for imbalanced and categorical data.
IGANI uses iterative GANs to improve traffic data imputation.
problem Imputation of traffic data in the absence of sensor data.
method Iterative Generative Adversarial Networks (IGANI) for unsupervised learning.
result IGANI produces more accurate imputation results compared to previous methods.
A new PCA-based imputation method for high-dimensional data.
problem Missing data in high-dimensional datasets.
method Principal Component Analysis Imputation (PCAI) framework.
result PCAI significantly speeds up imputation and maintains high accuracy.
Imputation for prediction often offers limited benefits, especially with powerful models.
problem The challenge of missing data in predictive models.
method Comparative analysis of imputation methods across various predictive models and datasets.
result Advanced imputation methods often offer limited benefits for powerful predictive models.
CrowdMI uses crowdsourcing to impute missing data, achieving results similar to complex models.
problem Missing data imputation using human crowdsourcing.
method Replicating a multiple imputation framework with multiple crowdworkers completing a survey.
result Valid imputations for both qualitative and quantitative missing data comparable to complex models.
Framework for imputing time series data with uncertainty measures.
problem Handling missing values in time series data, especially in healthcare.
method Uncertainty-aware multivariate time series imputation framework.
result Selective imputation of less uncertain values improves downstream tasks.
The paper compares methods for imputing missing categorical data in supervised classification tasks.
problem Missing data in categorical variables can degrade model performance.
method Experimented on two datasets with missing categorical data, comparing imputation methods with and without additional missing-data perturbation.
result Imputation methods can increase predictive accuracy in the presence of missing-data perturbation.
CSDI improves time series imputation by 40-65% over existing methods.
problem Imputing missing values in time series data.
method Conditional Score-based Diffusion models conditioned on observed data.
result CSDI improves by 40-65% over existing probabilistic imputation methods on popular metrics.
MTSCI uses diffusion models to impute multivariate time series data with consistency.
problem Imputation of missing values in multivariate time series data.
method MTSCI employs a contrastive complementary mask and mixup mechanism to ensure intra-consistency and inter-consistency.
result MTSCI achieves state-of-the-art performance on multivariate time series imputation tasks.
Study examines parallel computing strategies for faster imputation of missing data.
problem Time-consuming iterative imputation methods for large datasets.
method Variable-wise and model-wise distributed parallel computing strategies in missForest.
result Variable-wise distributed strategy introduces additional biases in imputation results.
A fast, non-iterative method for missing value imputation using random trees.
problem Missing value imputation in large and high-dimensional datasets.
method Recursive semi-random hyperplane cuts to assign observations to buckets and calculate weighted averages as imputations.
result Significantly faster than chained equations and scales well to large datasets.
Study compares imputation methods' effects on IML confidence intervals.
problem Missing data impacts IML interpretation and confidence intervals.
method Compared single vs multiple imputation methods on IML confidence intervals.
result Multiple imputation provides closer coverage to nominal than single imputation.
New research shows imputation and regression together can predict better than separate steps.
problem Predicting with data missing values without strong assumptions.
method Proposes a joint imputation and regression approach using NeuMiss neural network.
result Joint imputation and regression outperforms separate imputation and regression methods.
The paper compares theoretical and empirical performance of imputation methods for missing data.
problem Missing data in real-world datasets.
method Contrast of theoretical and empirical imputation methods for prediction.
result Mean-imputation is asymptotically optimal for prediction, while mode-imputation is sub-optimal.
New criteria improve imputation model selection using MOO.
problem Selecting the best imputation model using prediction accuracy metrics.
method Introduced three modified MOO criteria based on rank transformation, energy distance, and likelihood principle.
result Demonstrated how MOO is related to missing-at-random assumption and derived statistical and computational learning theories.
New algorithm improves data imputation for complex multimodal data sets.
problem Artifacts in imputation methods for multimodal distributions.
method Combines kNN and KDE for probabilistic estimates. result Lower imputation errors and higher likelihood estimates.
GANs improve missing data imputation for ranking tasks.
problem Missing data in ranking datasets violates standard assumptions.
method Conditional Imputation GAN for Extended Missing At Random and Extended Always Missing At Random mechanisms.
result Optimal GAN imputation for EMAR and EAMAR mechanisms.
Improved time series classification with imputed data using label-guided forest-based methods.
problem Missing data in time series data.
method Label-guided imputation using forest-based proximity measures.
result Imputation leads to higher classification accuracies, even with imputed values differing from true values.
Autoencoder improves imputation across various missing data types.
problem Efficiently impute missing data in diverse datasets.
method Developed a deep autoencoder framework for consistent training and imputation.
result Autoencoder outperformed state-of-the-art methods in all experiments.
IFGAN uses feature-specific GANs for missing value imputation.
problem Missing value imputation in data mining.
method Feature-specific Generative Adversarial Networks (GAN).
result IFGAN outperforms state-of-the-art algorithms in various missing conditions.
Random forest algorithms improve missing data imputation across various types.
problem Dealing with missing data in diverse datasets.
method Various RF imputation algorithms tested under different missing data mechanisms.
result RF imputation is generally robust and improves with increasing correlation.
New method for imputing missing data in multi-view data.
problem Large missing data sets in multi-view data.
method Stacked penalized logistic regression (StaPLR) in a dimension-reduced space.
result New method performs competitively with lower computational cost.
Kernel ridge regression imputation with consistent variance estimation for handling missing data.
problem Handling missing data in statistical analysis.
method Kernel ridge regression imputation combined with entropy method for variance estimation.
result Root-n consistency of the imputation estimator in a Sobolev space setting.
Paper introduces metrics to evaluate missing data imputation without ground truth.
problem Handling missing data in time series without ground truth.
method Introduces Wasserstein distance (WD) and Jensen-Shannon divergence (JSD) as metrics to evaluate imputation quality.
result WD and JSD are effective metrics for assessing missing data imputation quality.
Proposes a new imputation method using autoencoders and feedback mechanisms.
problem Missing data undermines the performance of downstream data products.
method Multiple Imputation with Denoising Autoencoders and Metamorphic Truth/Imputation Feedback.
result Outperforms other methods in various missingness mechanisms and data patterns.
Paper proposes methods to handle missing data in online RL, improving efficiency and uncertainty capture.
problem Missing data in online RL poses challenges due to the need to impute and act at each time step.
method Proposes fully online imputation ensembles and multiple imputation pathways to balance uncertainty and efficiency.
result Preliminary evidence suggests multiple imputation pathways can be a useful framework for simple and efficient online missing data RL methods.
GAIN uses GANs to impute missing data by forcing the generator to learn the true distribution.
problem Missing data in datasets.
method Adapting GAN framework to impute missing data, using a generator and discriminator.
result GAIN significantly outperforms state-of-the-art imputation methods on various datasets.
CollaGAN uses GANs to impute missing image data.
problem Missing data bias in applications requiring multiple inputs.
method Collaborative Generative Adversarial Network (CollaGAN) for multi-domain image imputation.
result CollaGAN produces higher quality imputed images than existing methods.
New online imputation method for mixed data improves accuracy and speed.
problem Missing value imputation in online settings for mixed data types.
method Online Gaussian copula model for imputation and change point detection.
result The model improves accuracy and speed, especially on large datasets.
NAOMI improves imputation accuracy for long-range sequences.
problem Missing value imputation in spatiotemporal data.
method Non-autoregressive deep generative model exploiting multiresolution structure.
result Significant improvement in imputation accuracy (60% reduction in average prediction error).
LSSDM improves imputation of multivariate time series data.
problem Imputation of multivariate time series data without labels.
method LSSDM projects observed data into latent space, reconstructs missing values without labels, and uses a conditional diffusion model for precise imputation.
result LSSDM achieves superior imputation performance and uncertainty analysis.
A new kNN imputation method improves classification performance on datasets with missing data.
problem Missing data in classification problems.
method Class weighted grey distance with MI weighting for kNN imputation.
result Improved classification performance compared to existing methods.
RISA improves VFL by using imputed samples with low uncertainty.
problem Limited overlapping samples constrain VFL performance.
method Imputing non-overlapping samples and using evidence theory to select reliable imputed samples.
result Significant performance gains achieved, especially with limited overlapping samples.
Imputation with missing-indicators improves machine learning performance, especially for categorical attributes.
problem Missing values in datasets can be lost through imputation, potentially reducing model performance.
method Combines missing-indicator approach with imputation strategies on real datasets.
result Missing-indicators generally increase classification performance, especially for categorical attributes.
Paper proposes a novel method to reduce mutual information for missing data imputation.
problem Missing data imputation in datasets with missingness patterns.
method Iterative minimization of KL divergence between imputed data and missingness mask, using rectified flow training objective.
result The proposed method achieves superior imputation performance on synthetic and real-world datasets.
RDIS fills missing values in time series data explicitly.
problem Missing values in time series data.
method Random Drop Imputation with Self-training.
result RDIS achieves competitive results on real-world datasets.