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.
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.
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.
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.
New framework predicts time series with missing values without imputation.
problem Predicting time series with missing values, especially when there's no ground truth for missing data.
method CRIB framework, combining attention mechanism and consistency regularization.
result CRIB framework predicts accurately even under high missing rates.
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.
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.
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.
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.
PAIR-CI calibrates CI tests for causal discovery with incomplete data.
problem Miscalibration of CI tests when imputing incomplete data.
method Integrates multiple imputation directly into the inferential procedure via a paired permutation design.
result PAIR-CI reduces false positive rates to below 5% in simulations.
A new imputation method estimates missing values by matching observed marginals from masked data.
problem Missing values in data undermine statistical and machine learning analysis.
method Estimates a distribution from masked observations using positive semi-definite kernel density estimation.
result The method yields both single and multiple imputations from the same fitted density, with statistical consistency and fast adaptive excess risk.
New method for fMRI missing value imputation improves robustness.
problem High frequency of missing values in fMRI data.
method Spatial and time-dependent regularization with a novel recurrent layer.
result Improved robustness against state-of-the-art alternatives.
Theoretical analysis of MCR for improving imputation quality in partially observed data.
problem Improving model generalization in partially observed settings.
method Theoretical analysis of Measure Consistency Regularization (MCR) for neural network distance.
result MCR's generalization advantage is not always guaranteed and can be monitored through a duality gap.
DRIO improves time series imputation by minimizing reconstruction error and distributional divergence.
problem Bias in imputation due to mismatch between observed and true data distributions.
method DRIO minimizes reconstruction error and worst-case divergence using Wasserstein ambiguity set.
result DRIO consistently provides robust imputation and improved forecasting.
Study improves healthcare time series imputation by considering structured missingness.
problem Structured missingness in clinical data impacts time series imputation models.
method Analysis of different masking strategies on imputation methods using PhysioNet Challenge 2012 dataset.
result Masking choices significantly affect imputation accuracy and clinical prediction.
In many application settings, the data have missing entries which make analysis challenging. An abundant literature addresses missing values in an inferential framework: estimating parameters and their variance from incomplete tables. Here, we consider supervised-learning settings: predicting a target when missing valu…
Noise in imputed values corrects biases in machine learning models.
problem Systematic biases in imputed values affect downstream analyses.
method Introducing noise to imputed values to correct biases.
result Noise-corrected imputation methods produce unbiased estimates.
The paper addresses missing data imputation issues by correcting for distribution shift.
problem Missing data imputation and the resulting distribution shift between observed and full data.
method Formulates imputation as a risk minimization problem and proposes a novel algorithm to correct for distribution shift.
result The proposed algorithm consistently improves imputation accuracy, reducing RMSE and Wasserstein distance by 3% and 7%, respectively.
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.
Deep learning models outperform MICE in large survey imputation but with hyperparameter tuning.
problem Comparing deep learning and MICE for missing data imputation in large surveys.
method Extensive simulation studies comparing four machine learning-based MI methods: MICE with classification trees, MICE with random forests, generative adversarial imputation networks, and multiple imputation using denoising autoencoders.
result MICE with classification trees consistently outperforms deep learning methods in terms of bias, mean squared error, and coverage.
MissBGM uses AI and Bayesian modeling for better missing data imputation.
problem Missing data imputation in data science, especially with uncertainty quantification.
method AI-powered Bayesian generative modeling with explicit modeling of missingness mechanisms.
result MissBGM provides principled posterior uncertainty over imputations and superior performance.
Paper proposes efficient algorithm for recovering sparsity pattern from deterministic missing data.
problem Recovering sparsity pattern from datasets with deterministic missing structure.
method Proposes an efficient algorithm for missing value imputation using topological property of censorship filter.
result Consistently recovers the sparsity pattern with high probability in polynomial time and logarithmic sample complexity.
PAIN network improves imputation for mixed datasets.
problem Missing data in diverse scientific domains.
method Dynamic adaptive imputation using statistical methods, random forests, and autoencoders.
result PAIN outperforms traditional imputation methods in preserving data distributions.
A new imputation method considers feature importance for missing value completion.
problem Missing values in datasets reduce accuracy and increase processing difficulty.
method Iterative matrix completion with feature importance learning.
result The method outperforms existing imputation algorithms on various datasets.
New algorithms handle missing data to improve fairness in machine learning.
problem Missing values in data can lead to unfair outcomes in machine learning models.
method Developed scalable and adaptive algorithms to handle missing values while preserving predictive information.
result Our adaptive algorithms consistently achieve higher fairness and accuracy than standard impute-then-classify methods.
Proposes a novel imputation network for clinical time series data.
problem Missing value imputation in clinical time series data with sparsity, irregularity, and high-dimensionality.
method Variational-recurrent imputation network that considers correlated features, temporal dynamics, and uncertainty.
result The proposed method outperformed state-of-the-art methods on real-world EHR datasets.
A new model for imputing missing values in time series data across domains.
problem Imputing missing values in time series data across domains with domain shifts and high missing rates.
method A diffusion-based imputation model that integrates shared spectral components and domain-specific temporal structures, with cross-domain consistency alignment.
result Our model effectively handles missing values and domain shifts, outperforming existing methods.
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.
Fed-MIWAE improves federated learning by imputing missing data.
problem Handling missing data in federated learning models.
method Federated version of MIWAE, a deep latent variable model for missing data imputation.
result Fed-MIWAE achieves imputation accuracy comparable to centralized methods in federated scenarios.
Improved functional data modeling with modern imputation methods.
problem Estimating complex non-linear models with sparsely and irregularly sampled functional data.
method Modified multiple imputation methods combining MissForest and Local Linear Forest.
result New imputation methods produce better estimates than existing methods.
New model handles missing data effectively in autoregressive models.
problem Handling missing data in autoregressive models.
method Reinterpret existing models through missing data lens, introduce principled framework for incomplete datasets, active information acquisition.
result MO-ARM consistently outperforms imputation baselines across real-world benchmarks.
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.
KReTTaH uses tensor trains and Hadamard overparameterization for fast, interpretable multi-way data imputation.
problem Multi-way data imputation for high-dimensional functional MRI and dynamic graph recovery.
method Reformulates imputation as RKHS regression with TT-constrained coefficients and Hadamard overparameterization. Optimizes TT coefficients and kernel matrices on Riemannian manifolds.
result Consistently outperforms state-of-the-art methods in modeling accuracy.
KReTTaH uses tensor trains and Hadamard overparameterization for fast, interpretable multi-way data imputation.
problem Multi-way data imputation in high-dimensional spaces.
method Reformulates imputation as RKHS regression with TT-constrained coefficients, optimized on manifold frameworks.
result Consistently outperforms state-of-the-art methods in accuracy.
FSD-CAP improves graph feature imputation under high missing rates.
problem Challenges in imputing missing node features in graphs, especially under high missing rates.
method Two-stage framework: subgraph expansion, fractional diffusion, class-aware propagation.
result Significantly improved imputation quality compared to existing methods, achieving high accuracy on benchmark datasets.
Sensor data has been playing an important role in machine learning tasks, complementary to the human-annotated data that is usually rather costly. However, due to systematic or accidental mis-operations, sensor data comes very often with a variety of missing values, resulting in considerable difficulties in the follow-…
Novel imputation method for EHRs with structured and sporadic missingness.
problem Missing data in integrated EHR datasets for clinical applications.
method Macomss, a novel imputation framework for structurally and heterogeneously missing data.
result Macomss outperforms existing methods in imputation and downstream prediction accuracy.
Simulation study evaluates tree-based imputation methods for multi-level data.
problem Ignoring dependencies in hierarchical data can compromise imputation accuracy.
method Chained Random Forests and Extreme Gradient Boosting (mixgb) adapted for multi-level data.
result Adapted boosting methods outperform traditional MICE for Level-1 variables at higher missingness rates.
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.
The paper develops methods to handle missing data using regularized M-estimation in reproducing kernel Hilbert space.
problem Handling missing data in statistical analysis.
method Kernel ridge regression for imputation and maximum entropy method for propensity score estimation.
result The proposed methods achieve statistical consistency and asymptotic equivalence.
We present a novel method named Latent Semantic Imputation (LSI) to transfer external knowledge into semantic space for enhancing word embedding. The method integrates graph theory to extract the latent manifold structure of the entities in the affinity space and leverages non-negative least squares with standard simpl…
This paper compares preprocessing techniques for XGBoost models on various data sets.
problem Improving predictive performance of XGBoost models through optimal data preprocessing.
method Comparison of feature selection, categorical handling, and null imputation methods.
result XGBoost importance by gain is the most consistent and highest-performing method for feature selection.
Paper uses ensemblers to predict sepsis early from patient records.
problem Early detection of sepsis in patients.
method Imputation and weak ensembler technique applied to 40k patient records.
result Model achieved 93.45% accuracy and 0.271 utility score.
A new method for filling in missing traffic data improves accuracy over existing techniques.
problem Incomplete spatiotemporal traffic data.
method Low-rank autoregressive tensor completion (LATC) framework.
result LATC framework better captures spatiotemporal consistency and local consistency.
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.
Model predicts missing boarding stops in smart card data.
problem Missing boarding stop information in public transport datasets.
method Supervised machine learning with ordinal classification.
result Proposed method significantly outperforms existing imputation methods.
New method predicts bankruptcy by imputing missing data with granular semantics.
problem Missing data, high dimensional data, and class imbalance in bankruptcy prediction.
method Granular computing for missing data imputation with feature semantics and AI-driven pipeline.
result Efficient solution for big datasets with high imputation rates.
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.