arXiv research

The study proposes algorithms to minimize rating discordance in missing data.

Sparse regression such as the Lasso has achieved great success in handling high-dimensional data. However, one of the biggest practical problems is that high-dimensional data often contain large amounts of missing values. Convex Conditioned Lasso (CoCoLasso) has been proposed for dealing with high-dimensional data with…

Merlin improves robustness of MTSF models to missing data.

Rating prediction is an important application, and a popular research topic in collaborative filtering. However, both the validity of learning algorithms, and the validity of standard testing procedures rest on the assumption that missing ratings are missing at random (MAR). In this paper we present the results of a us…

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

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

Recommending items to users is a challenging task due to the large amount of missing information. In many cases, the data solely consist of ratings or tags voluntarily contributed by each user on a very limited subset of the available items, so that most of the data of potential interest is actually missing. Current ap…

Given $n$ samples from a population of individuals belonging to different types with unknown proportions, how do we estimate the probability of discovering a new type at the $(n+1)$-th draw? This is a classical problem in statistics, commonly referred to as the missing mass estimation problem. Recent results by Ohannes…

BlockEcho method improves imputation of block-wise missing data.

IFGAN uses feature-specific GANs for missing value imputation.

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

New method for tensor classification with missing data.

Study compares traditional and machine learning methods for handling missing data in longitudinal studies.

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…

New estimator for symmetric kernel expectations, robust to missing data.

New method for efficient matrix completion with nonignorable missing data.

We propose a simple imputation method for high-dimensional linear regression with missing data.

A new criterion HBIC improves model selection for factor analysis with missing data.

GT estimator shows convergence for Markov samples, improving i.i.d. results.

New method resolves bias in recommender learning without needing missing data.

Paper tackles missing data shifts in domains, showing how to adapt models.

Missing values widely exist in many real-world datasets, which hinders the performing of advanced data analytics. Properly filling these missing values is crucial but challenging, especially when the missing rate is high. Many approaches have been proposed for missing value imputation (MVI), but they are mostly heurist…

Paper proposes a new model for imputing missing spatiotemporal traffic data.

New Fourier transform method handles missing data and asynchronous observations.

Paper proposes a new tensor imputation method for spatiotemporal traffic data with missing patterns.

Paper tackles linear models with missing values, achieving minimax optimal results.

Study proposes GRU-D networks for missing value handling in road surface friction prediction.

Missing responses is a missing data format in which outcomes are not always observed. In this work we develop kernel machines that can handle missing responses. First, we propose a kernel machine family that uses mainly the complete cases. For the quadratic loss, we then propose a family of doubly-robust kernel machine…

Multimodal learning has shown promising performance in content-based recommendation due to the auxiliary user and item information of multiple modalities such as text and images. However, the problem of incomplete and missing modality is rarely explored and most existing methods fail in learning a recommendation model …

Although a majority of the theoretical literature in high-dimensional statistics has focused on settings which involve fully-observed data, settings with missing values and corruptions are common in practice. We consider the problems of estimation and of constructing component-wise confidence intervals in a sparse high…

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-…

Study examines how imputation accuracy affects prediction accuracy in regression problems with missing covariates.

New framework predicts time series with missing values without imputation.

The paper controls false discovery rate in link prediction using conformal inference.

This paper tackles missing data in Burundian bond market yield curves.

Latent diffusion improves robustness in missing data imputation.

In many machine learning applications, we are faced with incomplete datasets. In the literature, missing data imputation techniques have been mostly concerned with filling missing values. However, the existence of missing values is synonymous with uncertainties not only over the distribution of missing values but also …

OnlineSCI extends ACI for adaptive selective inference with improved coverage and IER control.

Matrix completion works well for smooth non-linear structures, even without low-rank assumptions.

Proposes a new jackknife method for time series hyperparameter selection.

Predict missing movie ratings or graph embeddings with low rank matrices.

CoIFNet unifies imputation and forecasting for robust multivariate time series prediction with missing values.

FSD-CAP improves graph feature imputation under high missing rates.

Paper presents a method for imputing and forecasting structural response from incomplete sensor data.

A new kNN imputation method improves classification performance on datasets with missing data.

Missing data is an expected issue when large amounts of data is collected, and several imputation techniques have been proposed to tackle this problem. Beneath classical approaches such as MICE, the application of Machine Learning techniques is tempting. Here, the recently proposed missForest imputation method has show…

PC-GAIN improves GAIN's imputation by incorporating category information.

TMLE improves causal effect estimation in missing data scenarios with various positivity violations.