Federated learning method improves covariate shift adaptation for missing target values.
problem Missing target values in federated learning.
method Federated covariate shift adaptation algorithm for missing target output values.
result Asymptotically unbiased and efficient algorithm for federated learning.
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…
Paper tackles missing data shifts in domains, showing how to adapt models.
problem Missing data mechanisms differ between source and target domains.
method Introduced DAMS, showing covariate shift under certain conditions, and providing adjustments for missingness.
result Simple adjustments can improve target model performance even when missingness rates vary.
In this paper, we show the spooky effect at a distance that arises in optimal estimation of multiple targets with the optimal sub-pattern assignment (OSPA) metric. This effect refers to the fact that if we have several independent potential targets at distant locations, a change in the probability of existence of one o…
Estimates vaccine effectiveness and immune correlates in TND studies with missing data.
problem Confounding and missing data in TND studies of vaccine effectiveness and immune correlates.
method Targeted maximum likelihood estimation using a semiparametric logistic regression model.
result Valid causal inference of vaccine effectiveness and immune correlates in TND studies with missing exposure data.
Missing data is a pervasive problem in data analyses, resulting in datasets that contain censored realizations of a target distribution. Many approaches to inference on the target distribution using censored observed data, rely on missing data models represented as a factorization with respect to a directed acyclic gra…
DRUM transfers cardiac arrest models across registries with missing covariates.
problem Clinical prediction models fail when key training covariates are unavailable at deployment.
method DRUM transfers models to target populations with structurally missing covariates, optimizing worst-case predictive performance over unknown target distributions.
result DRUM yields better-calibrated predictions and improved clinical classification performance across sites.
Method tackles missing covariates in large-scale datasets.
problem Cross-population missing data problem in large-scale datasets.
method Augmented transfer regression learning method combining importance-weighted estimating equations and imputation terms.
result Estimator is n1/2-consistent and asymptotically normal, attaining semiparametric efficiency bound under correct specification. Proposes a method to select features for subgroup datasets with systematic missing data.
problem Feature selection for datasets with subgroup structure and systematic missing data.
method Develops a heterogeneous graph neural network to propagate information between feature-subgroup-target variable connections.
result Demonstrates improved feature selection performance and scalability.
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 …
Method learns feature map between source and target domains for high-dimensional regression with missing features.
problem High-dimensional regression with differing feature sets in target and source domains.
method First learns a feature map between missing and observed features using source data, then imputes missing features in target domain, and performs two-step transfer learning for penalized regression.
result Developed upper bounds on estimation and prediction errors for HTL, showing dependence on model complexity, sample size, feature map quality, and domain differences.
Emputation learns imputation models guided by missingness assumptions.
problem Learning imputation models for missing data given observed data.
method Guided by specific missingness assumptions, Emputation trains a deep generative model to learn the extrapolation distribution of missing variables.
result The population minimizer of the emputation risk recovers the target extrapolation distribution under various identification assumptions.
AugMask trains diffusion models on incomplete tabular data by augmenting missing values and applying denoising supervision.
problem Training diffusion models on incomplete tabular data with missing values.
method AugMask uses stochastic augmentation and denoising supervision to adapt diffusion models to incomplete data.
result AugMask enables diffusion-based tabular generators to outperform specialized missing-aware baselines across various datasets and missingness regimes.
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…
Study shows linear predictors fail with missing data, but simpler approximations and neural networks can work.
problem Building predictors with missing data when the target is a linear function of observed data.
method Analyzed the Gaussian case and proposed a linear function of multiway interactions. Studied a simple approximation and proved generalization bounds. Showed multilayer perceptrons with ReLU activation can be consistent.
result Simple approximations and neural networks can be effective in handling missing data, especially with sufficient data.
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a.…
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.
The paper studies OPE with missing data, showing bias under nonignorable missingness and proposing a solution.
problem Estimating value of a target policy from logged data with missingness.
method Investigates OPE with monotone missingness, proposes an IPW value estimator, and conducts statistical inference.
result Value estimates remain unbiased under ignorable missingness but can be biased under nonignorable missingness.
New framework estimates target functions from incomplete data.
problem Estimating target functions from partially observed data.
method IF-learning framework using influence functions.
result Two learning algorithms developed for estimation.
A new method for handling missing values in data.
problem Handling missing values in machine learning models.
method Sharing pattern submodels with sparsity-inducing regularization.
result Sharing pattern submodels provide robust predictions and maintain/improve pattern submodel performance.
Neural networks designed for the task of classification have become a commodity in recent years. Many works target the development of more effective networks, which results in a complexification of their architectures with more layers, multiple sub-networks, or even the combination of multiple classifiers, but this oft…
Paper proposes methods to learn accurate models from incomplete data without imputation.
problem Learning accurate models from datasets with missing values.
method Unified approach for checking data imputation necessity and efficient algorithms.
result Significant reduction in time and effort needed for data imputation.
JoLT uses LLMs to make probabilistic predictions on tabular data.
problem Making probabilistic predictions on tabular data efficiently and without preprocessing.
method JoLT leverages LLMs' in-context learning to define joint distributions over tabular data.
result JoLT outperforms other methods on tabular classification and regression tasks.
Model predicts future values and fills in missing data.
problem Missing data in time series data.
method End-to-end time series model with two neural networks.
result Framework performs well in imputation and forecasting.
GRAPE uses graph representation to handle missing data in feature imputation and label prediction.
problem Handling missing data in machine learning tasks.
method GRAPE uses a bipartite graph where observations and features are nodes, and observed feature values are edges. It formulates feature imputation as edge-level prediction and label prediction as node-level prediction, solving these with Graph Neural Networks.
result GRAPE achieves 20% lower mean absolute error for imputation and 10% lower for label prediction compared to state-of-the-art methods.
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.
Optimal transfer learning for missing not-at-random matrix completion using source data.
problem Matrix completion in a Missing Not-at-Random setting with incomplete and noisy source data.
method Active sampling of rows and columns, feature shift in latent space, minimax lower bounds, computationally efficient estimation framework.
result Achieves minimax lower bound for active sampling setting, avoiding incoherence assumptions.
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.
Bayes predictor remains robust to ignorable missingness shifts.
problem Challenges in prediction with missing covariates and shifts in missingness reasons.
method Bayesian approach and different prediction methods.
result Bayes predictor remains unchanged by ignorable shifts, but robust prediction requires disregarding missingness for non-ignorable shifts.
Two-stage TMLE reduces bias and improves efficiency in CRTs.
problem Differential outcome measurement and imbalance in baseline predictors in CRTs.
method Two-stage targeted minimum loss-based estimator (TMLE) to adjust for baseline covariates.
result Our approach nearly eliminates bias due to differential outcome measurement.
In target tracking, the estimation of an unknown weaving target frequency is crucial for improving the miss distance. The estimation process is commonly carried out in a Kalman framework. The objective of this paper is to examine the potential of using neural networks in target tracking applications. To that end, we pr…
The paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at prediction time. By building on the ensembling properties of Dropout regularization, we propose a methodology, named DropIn, which efficiently t…
The paper analyzes an actor-critic algorithm with target networks for deep reinforcement learning.
problem Lack of theoretical understanding of target networks in actor-critic methods.
method Proposes a theoretical analysis of an online target-based actor-critic algorithm with linear function approximation.
result Establishes asymptotic convergence results and finite-time analysis for both critic and actor.
Machine learning improves sub-hourly precipitation data recovery.
problem Missing precipitation data at sub-hourly intervals.
method Two-step process: rain/non-rain classification and rain intensity prediction.
result Machine learning outperforms traditional methods in predicting missing precipitation data.
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.
New estimator handles covariate shift with closed-form solution and super-efficiency.
problem Handling covariate shift in missing data and causal inference problems.
method Minimum Wasserstein distance estimation framework.
result Closed-form expression and super-efficiency relative to semiparametric efficient estimator.
Paper simplifies balancing weights by relaxing outcome assumptions.
problem Estimating missing outcomes in a target population.
method Relaxes outcome assumptions to simplify balancing weights.
result Balancing weights can be simplified with convex loss and minimum worst-case bias.
Recent unsupervised approaches to domain adaptation primarily focus on minimizing the gap between the source and the target domains through refining the feature generator, in order to learn a better alignment between the two domains. This minimization can be achieved via a domain classifier to detect target-domain feat…
Bedside monitors in Intensive Care Units (ICUs) frequently sound incorrectly, slowing response times and desensitising nurses to alarms (Chambrin, 2001), causing true alarms to be missed (Hug et al., 2011). We compare sliding window predictors with recurrent predictors to classify patient state-of-health from ICU multi…
Study uses surrogate data to improve treatment effect estimation with scarce outcome data.
problem Limited outcome data hinders estimating treatment effects.
method Uses abundant surrogate data to estimate treatment effects without stringent assumptions.
result Improves precision of treatment effect estimation.
The Long Short-Term Memory (LSTM) neural network based data association algorithm named as DeepDA for multi-target tracking in clutters is proposed to deal with the NP-hard combinatorial optimization problem in this paper. Different from the classical data association methods involving complex models and accurate prior…
HappyMap improves fairness and learning across domains by generalizing multi-calibration.
problem Improving fairness and learning across different domains for predictions.
method Proposes HappyMap, a generalized approach to multi-calibration.
result Unified understanding of fairness and learning across domains.
The premorbid geometry of the mandible is of significant relevance in jaw reconstructive surgeries and occasionally unknown to the surgical team. In this paper, an optimization framework is introduced to train deep models for completion (reconstruction) of the missing segments of the bone based on the remaining healthy…
EnMDAP aligns conditional distributions for multi-source domain adaptation using pseudolabels.
problem Training a target model with no labeled data in the absence of target data labels.
method EnMDAP uses label-wise moment matching and ensemble learning with multiple feature extractors.
result EnMDAP achieves state-of-the-art performance in multi-source domain adaptation tasks.
Generalization and adaptation of learned skills to novel situations is a core requirement for intelligent autonomous robots. Although contextual reinforcement learning provides a principled framework for learning and generalization of behaviors across related tasks, it generally relies on uninformed sampling of environ…
A new model predicts in-hospital mortality from EHR data, handling missing values with uncertainty.
problem Handling missing values in non-stationary, noisy EHR data for accurate mortality prediction.
method Uncertainty-Gated Stochastic Sequential Model using variational recurrent networks.
result The model outperforms state-of-the-art methods in predicting in-hospital mortality.
Model predicts unseen climate extremes to inform risk planning.
problem Missing unseen climate extremes in historical records.
method DeepX-GAN model capturing spatial dependence.
result Unseen heat extremes disproportionately threaten vulnerable regions.
Targeted Learning uses robust statistics for reproducible research.
problem Improving reproducibility and rigor in statistical analyses.
method Principled standard for statistical estimation and inference, minimizing assumptions.
result Enhances reliability of statistical conclusions.