arXiv research

Study on missing data mechanisms and simple imputation methods in fairness of machine learning algorithms.

Missing values challenge data analysis because many supervised and unsupervised learning methods cannot be applied directly to incomplete data. Matrix completion based on low-rank assumptions are very powerful solution for dealing with missing values. However, existing methods do not consider the case of informative mi…

New random forest algorithm improves regression with missing data.

DeepIFSAC uses attention mechanisms and contrastive learning to impute missing values in tabular data.

IFGAN uses feature-specific GANs for missing value imputation.

New framework predicts time series with missing values without imputation.

Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements. Missing values can complicate the application of clustering algorithms, whose goals are to group points based on some similarity criterion. A c…

New methods for identifying and estimating missing data under complex mechanisms.

NeuMiss networks tackle supervised learning with missing values, offering efficient and robust predictions.

MissBGM uses AI and Bayesian modeling for better missing data imputation.

A probabilistic query may not be estimable from observed data corrupted by missing values if the data are not missing at random (MAR). It is therefore of theoretical interest and practical importance to determine in principle whether a probabilistic query is estimable from missing data or not when the data are not MAR.…

This work addresses missing data imputation for MNAR scenarios with identifiable deep generative models.

New model recognizes emotions with missing modalities, improving accuracy.

Generative imputation network improves missing value estimation.

Improved traffic forecasting model handles missing data.

Adapts conformal prediction for missing data, ensuring valid coverage.

This paper tackles time series imputation by identifying and modeling different missing mechanisms.

Proposes methods to handle missing data in clustering models.

Ranked data appear in many different applications, including voting and consumer surveys. There often exhibits a situation in which data are partially ranked. Partially ranked data is thought of as missing data. This paper addresses parameter estimation for partially ranked data under a (possibly) non-ignorable missing…

A novel k-means method for MNAR data improves clustering accuracy.

Paper explores Bayes rule for Gaussian mixtures with missing data, outperforming supervised classifiers.

Missing data enhances privacy in differential privacy.

New research shows imputation and regression together can predict better than separate steps.

Unified diffusion model tackles missing data in tabular datasets.

Efficiently clusters incomplete data without imputation or full EM, faster and more accurate.

Missing data are ubiquitous in many domains including healthcare. When these data entries are not missing completely at random, the (conditional) independence relations in the observed data may be different from those in the complete data generated by the underlying causal process. Consequently, simply applying existin…

MissNODAG learns cyclic causal graphs from incomplete data.

Matrix completion is a modern missing data problem where both the missing structure and the underlying parameter are high dimensional. Although missing structure is a key component to any missing data problems, existing matrix completion methods often assume a simple uniform missing mechanism. In this work, we study ma…

A deep generative model improves imputation of MNAR data by treating missing and complete data equally.

New method for efficient matrix completion with nonignorable missing data.

A new method for semi-supervised learning with missing data using GMM and margin confidence.

We propose a general, theoretically justified mechanism for processing missing data by neural networks. Our idea is to replace typical neuron's response in the first hidden layer by its expected value. This approach can be applied for various types of networks at minimal cost in their modification. Moreover, in contras…

Extraction of missing attribute values is to find values describing an attribute of interest from a free text input. Most past related work on extraction of missing attribute values work with a closed world assumption with the possible set of values known beforehand, or use dictionaries of values and hand-crafted featu…

We present a general framework, the coupled compound Poisson factorization (CCPF), to capture the missing-data mechanism in extremely sparse data sets by coupling a hierarchical Poisson factorization with an arbitrary data-generating model. We derive a stochastic variational inference algorithm for the resulting model …

GANs improve missing data imputation for ranking tasks.

An accurate road surface friction prediction algorithm can enable intelligent transportation systems to share timely road surface condition to the public for increasing the safety of the road users. Previously, scholars developed multiple prediction models for forecasting road surface conditions using historical data. …

The time series cluster kernel (TCK) provides a powerful tool for analysing multivariate time series subject to missing data. TCK is designed using an ensemble learning approach in which Bayesian mixture models form the base models. Because of the Bayesian approach, TCK can naturally deal with missing values without re…

MTSCI uses diffusion models to impute multivariate time series data with consistency.

Gaussian Processes improve missing value imputation in datasets.

Paper tackles informative labels in semi-supervised learning, proposing debiasing methods.

Many real datasets contain values missing not at random (MNAR). In this scenario, investigators often perform list-wise deletion, or delete samples with any missing values, before applying causal discovery algorithms. List-wise deletion is a sound and general strategy when paired with algorithms such as FCI and RFCI, b…

kNNSampler imputes missing values from their distributions using kNN.

New algorithms handle missing outcomes in MAB, reducing regret.

The paper analyzes $k$-means clustering for missing data, proving statistical guarantees under MCAR.

Proposes a new imputation method using autoencoders and feedback mechanisms.

Double autoencoder $Ae^2I$ improves missing value imputation in recommender systems.

ELMV uses ensemble learning to handle missing values in EHR data.

Interpretable ML models for missing data and visualisation.