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.
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.
Multivariate time series with missing values are common in areas such as healthcare and finance, and have grown in number and complexity over the years. This raises the question whether deep learning methodologies can outperform classical data imputation methods in this domain. However, naive applications of deep learn…
Selective imputation improves treatment effect estimation from missing data.
problem Missing data complicates treatment effect estimation, especially with treatment variables.
method Introduced mixed confounded missingness (MCM) and selective imputation.
result Selective imputation provides unbiased treatment effect estimates.
Imputation-Powered Inference improves subpopulation efficiency in missing data settings.
problem Complex missing data patterns challenge standard inference methods.
method Imputation-Powered Inference (IPI) combines blackbox imputation with bias correction.
result IPI provides valid and efficient M-estimation under MCAR blockwise missingness.
We tackle missing data in SBI methods and introduce a neural process approach.
problem Missing data in SBI methods can bias parameter estimation.
method We introduce a neural process approach to jointly learn imputation and inference.
result Our method provides robust inference outcomes compared to baselines.
A deep generative model improves imputation of MNAR data by treating missing and complete data equally.
problem Missing Not At Random (MNAR) data in analysis.
method A generative model-specific joint probability decomposition method (conjunction model) and a deep generative imputation model (GNR).
result GNR surpasses state-of-the-art MNAR baselines with significant margins in RMSE and better mask reconstruction.
Unified ML imputation framework for missing data.
problem Invalid inference with incomplete data and ML imputations.
method Stratified by missingness patterns, weights and aggregates complete case information.
result Valid statistical inference for general missingness patterns.
In this article, we investigate the features which enhanced discriminate the survival in the micro and small business (MSE) using the approach of data mining with feature selection. According to the complexity of the data set, we proposed a comparison of three data imputation methods such as mean imputation (MI), k-nea…
New AR framework handles missing values for better recourse actions.
problem Existing AR methods fail with missing values, leading to invalid or costly actions.
method Formulates task using multiple imputation and mixed-integer linear optimization.
result Efficacy of new method demonstrated in experiments.
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.
Proposes a method to handle missing inputs in Bayesian optimization.
problem Missing values in historical data and function evaluations.
method Impute missing values using probability distributions and develop a new acquisition function.
result Improves performance of Bayesian optimization by handling missing inputs effectively.
The problem of machine learning with missing values is common in many areas. A simple approach is to first construct a dataset without missing values simply by discarding instances with missing entries or by imputing a fixed value for each missing entry, and then train a prediction model with the new dataset. A drawbac…
Proposes a framework to handle missing data in traffic forecasting with sensor blackouts.
problem Missing data in traffic forecasting due to sensor blackouts, especially when correlated with traffic conditions.
method Latent state-space framework that models traffic dynamics and sensor dropout probabilities.
result Improves traffic forecasting by reducing blackout imputation RMSE from 7.02 to 4.23, with MNAR modeling providing additional gains.
AI-generated variables bias regression estimates; methods correct for invalid inference.
problem Bias in regression estimates due to AI-generated variables.
method Two methods: bias correction and joint estimation.
result Valid inference restored through proposed methods.
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.
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.
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.
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.
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.
Missing data imputation can help improve the performance of prediction models in situations where missing data hide useful information. This paper compares methods for imputing missing categorical data for supervised classification tasks. We experiment on two machine learning benchmark datasets with missing categorical…
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.
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.
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.
This work proposes a non-iterative strategy for missing value imputations which is guided by similarity between observations, but instead of explicitly determining distances or nearest neighbors, it assigns observations to overlapping buckets through recursive semi-random hyperplane cuts, in which weighted averages are…
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.
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.
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.
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.
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.
Imputation of missing data is a common application in various classification problems where the feature training matrix has missingness. A widely used solution to this imputation problem is based on the lazy learning technique, k-nearest neighbor (kNN) approach. However, most of the previous work on missing data does…
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.
tBayes-MICE uses Bayesian MICE for time series data imputation.
problem Missing data in time series data.
method Bayesian MICE with MCMC, temporal features, and different samplers.
result tBayes-MICE reduces imputation errors and accounts for uncertainty.
Imputation method respects manifold structure for missing data.
problem Missing data imputation in high-dimensional data.
method Model-based imputation using mixture variational autoencoders and sampling-importance-resampling (SIR).
result Competitive performance and uncertainty quantification in imputations.