This paper tackles time series imputation by identifying and modeling different missing mechanisms.
problem Different types of missing mechanisms (MAR, MNAR) in time series data.
method Proposes a framework for time series imputation by analyzing data generation processes and modeling latent variables via variational inference and normalizing flow.
result Establishes identifiability results for latent variables under nonlinear independent component analysis, showing that latent variables are identifiable.
Paper proposes methods for estimating partially ranked data with graph regularization.
problem Estimating parameters for partially ranked data with missing data.
method Graph regularization in conjunction with Expectation-Maximization algorithm.
result The proposed estimators work well under non-ignorable missing mechanisms.
Paper explores Bayes rule for Gaussian mixtures with missing data, outperforming supervised classifiers.
problem Improving classification accuracy in partially classified samples with missing data.
method Generative model framework with missing-data mechanism, Bayes rule allocation.
result Bayes rule classifier with missing-data mechanism outperforms fully supervised classifiers in various conditions.
Study on missing data mechanisms and simple imputation methods in fairness of machine learning algorithms.
problem Impact of missing data mechanisms and simple imputation methods on fairness of machine learning algorithms.
method Three popular datasets for classification fairness were used. Missing values were generated using three missing data mechanisms. Various missing data handling techniques (listwise deletion, mean imputation, mode imputation, multiple imputation) were applied to the datasets. Fairness was assessed using classification algorithms (random forests).
result Missing data mechanism does not significantly impact fairness; listwise deletion gives highest fairness on average.
New method for matrix completion under complex missing data patterns.
problem Matrix completion with complex missing data patterns.
method Estimate the probability matrix of observation via low-rank matrix estimation and use inverse probabilities weighting to complete the target matrix.
result Optimal asymptotic convergence rates for observation probabilities and target matrix estimation.
New method for efficient matrix completion with nonignorable missing data.
problem Nonignorable missing data in matrix completion.
method Nuclear norm regularized U-statistic loss function and accelerated proximal gradient algorithm.
result Near minimax optimal statistical convergence rate for nonignorable missing data.
A new method for semi-supervised learning with missing data using GMM and margin confidence.
problem Handling missing data in semi-supervised learning with classification uncertainty.
method Explicitly models missingness mechanism, uses margin confidence and Aranda Ordaz function, develops ECM algorithm.
result Effective reduction of bias and robustness in semi-supervised learning with substantial missing labels.
Framework captures missing data in sparse data sets.
problem Capturing missing data in extremely sparse data sets.
method Coupled compound Poisson factorization with stochastic variational inference.
result Explicitly modeling missing data improves results in clustering, prediction, and matrix factorization.
Paper proposes methods to handle missing values in clinical data.
problem Missing Not At Random (MNAR) data in clinical studies.
method Model-based and surrogate estimation strategies for low-rank matrix completion.
result Proposed methods can handle different missing value mechanisms robustly.
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.
New methods for identifying and estimating missing data under complex mechanisms.
problem Missing data mechanisms dependent on missing values themselves.
method Developed a new MNAR model and proposed semiparametric estimation methods.
result Established sufficient conditions for identifying complete-data distribution and missingness mechanism.
Paper tackles informative labels in semi-supervised learning, proposing debiasing methods.
problem Informative labels can bias semi-supervised learning models, especially when some classes are more likely to be labeled.
method Estimates missing-data mechanism and uses inverse propensity weighting to debias SSL algorithms.
result Proposed methods improve SSL performance, demonstrated on various datasets including medical ones.
Algorithm determines if missing data makes probabilistic queries estimable.
problem Determining estimability of probabilistic queries from missing data.
method Algorithm based on Bayesian networks and missingness mechanisms.
result Advances existing work by systematically determining estimability.
New random forest algorithm improves regression with missing data.
problem Regression with missing data values.
method New random forest algorithm compared to existing techniques.
result Improved performance in quadratic errors and bias compared to existing methods.
The paper analyzes k k k -means clustering for missing data, proving statistical guarantees under MCAR.
problem Statistical guarantees for k k k -means clustering with missing data, especially under Missing Completely at Random (MCAR). method Established n \sqrt{n} n -excess risk bound and consistency of cluster centers under general missing mechanisms; derived n \sqrt{n} n -convergence rate and asymptotic normality for MCAR. result Achieving n \sqrt{n} n -rate and converging to true cluster centers requires distinct true cluster centers in every dimension under MCAR. New algorithms handle missing outcomes in MAB, reducing regret.
problem Missing outcomes in real-world MAB scenarios lead to biased estimates and linear regret.
method Introduced algorithms for MAR and MNAR missingness mechanisms in MAB.
result Significant improvements in decision-making by accounting for missingness.
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 tackles missing data in high-dimensional datasets using deep learning and swarm intelligence.
problem Missing data in high-dimensional datasets, considering different mechanisms.
method Combines deep learning and swarm intelligence to estimate missing data.
result Promising approach to estimate missing data, despite longer running times.
Autoencoder neural network is implemented to estimate the missing data. Genetic algorithm is implemented for network optimization and estimating the missing data. Missing data is treated as Missing At Random mechanism by implementing maximum likelihood algorithm. The network performance is determined by calculating the…
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.
DeepIFSAC uses attention mechanisms and contrastive learning to impute missing values in tabular data.
problem Missing values in tabular data, especially when high and not random.
method Row and column attention in a contrastive learning framework with CutMix data augmentation.
result Proposed method outperforms state-of-the-art methods for missing rates between 10% and 90% and various missing value types.
Adapts conformal prediction for missing data, ensuring valid coverage.
problem Uncertainty quantification with missing covariates.
method Proposes a reweighted conformal prediction procedure for handling missing values.
result Guaranteed Marginal Coverage and Mask-Conditional Validity for general missing data mechanisms.
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.
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.
Proposes methods to handle missing data in clustering models.
problem Missing data, especially MNAR, hinders model-based clustering.
method Developed a mixture model for different types of data, including MNAR, using Expectation Maximization algorithm.
result The proposed MNARz model simplifies inference and enables clustering with MNAR data.
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.
Proposes methods to correct bias and missing data in regression models.
problem Nonignorable selection bias and missing response in regression models.
method Imputation-based and importance weighted regression methods, including repeated regression and doubly robust combination.
result Repeated regression can effectively correct bias and outperforms weighted regression in extrapolation.
A deep learning framework discovers causal relationships from incomplete data.
problem Discovering causal knowledge from incomplete observational data.
method Imputated Causal Learning (ICL) framework for iterative missing data imputation and causal structure discovery.
result ICL outperforms state-of-the-art methods in various missing data scenarios.
NeuMiss networks tackle supervised learning with missing values, offering efficient and robust predictions.
problem Challenges in supervised learning with missing values, especially when the response is a linear function of the complete data.
method Derive analytical form of optimal predictor under linearity assumption and various missing data mechanisms. Propose NeuMiss networks using multiplication by missingness indicator.
result Upper bound on Bayes risk and good predictive accuracy with independent complexity of missing data patterns.
Algorithm identifies missing data distributions in graphical models.
problem Identifying missing data distributions in graphical models with interventionist perspective.
method Tree-based identification algorithm that tracks selection bias and admissible intervention strategies.
result Valid estimating equations for missingness mechanism and complete data distribution.
New method for causal discovery in presence of missing data.
problem Missing data complicates causal inference.
method Developed MVPC algorithm to handle different missingness mechanisms.
result MVPC gives asymptotically correct results even on MAR or MNAR data.
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.
This work addresses missing data imputation for MNAR scenarios with identifiable deep generative models.
problem Missing data with complex missingness mechanisms (MNAR) leading to biased imputation results.
method Systematic analysis and proposal of an identifiable deep generative model.
result Proposed model provides identifiability guarantees under mild assumptions for various MNAR mechanisms.
New method for contextual bandit with missing rewards.
problem Contextual bandit with missing rewards in online settings.
method Combining contextual bandit approach with unsupervised learning (clustering) to estimate missing rewards.
result Promising empirical results on real-life datasets.
Missing data enhances privacy in differential privacy.
problem Privacy preservation in datasets with missing values.
method Formalized missing data as a privacy amplification mechanism within differential privacy.
result Incomplete data can yield privacy amplification for differentially private algorithms.
TMLE improves causal effect estimation in missing data scenarios with various positivity violations.
problem Estimating causal effects in studies with missing data and positivity violations.
method Targeted Maximum Likelihood Estimation (TMLE) with various missing data methods.
result Complete cases with TMLE incorporating an outcome-missingness model exhibit lower bias and greater robustness against positivity violations.
Neural networks improve missing data processing by predicting expected values.
problem Handling missing data in neural networks.
method Replace neuron's response in the first hidden layer with its expected value.
result Our method outperforms typical imputation strategies and other incomplete data methods.
Deep learning model predicts ICU mortality by attending to missing data.
problem Handling irregular and missing data in ICU physiological time-series.
method Long Short-Term Memory architecture with attention mechanisms.
result Competitive and interpretable results on PhysioNet 2012 dataset.
Study proposes GRU-D networks for missing value handling in road surface friction prediction.
problem Missing values in road surface friction data affect prediction accuracy.
method Gated Recurrent Unit (GRU) network with decay mechanism.
result GRU-D networks outperform baseline models in road surface friction prediction.
We focus on an interpolation method referred to Bayesian reconstruction in this paper. Whereas in standard interpolation methods missing data are interpolated deterministically, in Bayesian reconstruction, missing data are interpolated probabilistically using a Bayesian treatment. In this paper, we address the framewor…
Optimal clustering handles missing values without imputation.
problem Missing values complicate clustering algorithms in biomedical studies.
method Integrates missing value mechanism into optimal clustering framework.
result Superior performance compared to other clustering approaches.
MissNODAG learns cyclic causal graphs from incomplete data.
problem Causal discovery in systems with feedback loops and missing data.
method Differentiable framework integrating additive noise model and expectation-maximization.
result MissNODAG uncovers cyclic structures and missingness mechanisms from partially observed data.
Generative imputation network improves missing value estimation.
problem Missing data in statistical analysis.
method Integrates generative networks with chained equations for robust multiple imputation.
result GCMI outperforms other imputation techniques in simulations and real data.
New model recognizes emotions with missing modalities, improving accuracy.
problem Handling missing modalities in emotion recognition.
method Transformer-based architecture with cross-attention and self-attention mechanisms.
result Improvement of 37% in predicting arousal values and 30% in valence values compared to baseline.
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.
Bounds and sensitivity analysis for causal effects with MNAR confounders.
problem Estimating causal effects with missing outcome data.
method Assumption-free bounds and sensitivity analysis for outcome-independent MNAR.
result Valid bounds and sensitivity analysis methods for causal effect estimation.
Semi-supervised learning benefits from informative missing labels, improving classifier performance.
problem Missing labels in semi-supervised learning can be informative, improving classifier performance.
method Formulates missingness as a mixture model problem and uses EM algorithm for fitting.
result Modelling informative missingness can yield a classifier with smaller expected error than a completely labelled sample.
Improved traffic forecasting model handles missing data.
problem Short-term traffic forecasting with missing values.
method Proposed SBU-LSTM architecture with bidirectional and unidirectional LSTM.
result Superior performance in accuracy and robustness for network-wide traffic prediction.