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#missing-data

4 curated events
papersTODAY 04:00 UTC

Shapley Value Estimators Adapted for Multi-Site Data with Missing Features

A new arXiv paper addresses a limitation in population-level Shapley value estimation, a common approach for attributing feature importance in machine learning models. Existing estimators typically require fully observed data when evaluating the coalitional game, which fails when features are missing in blocks across multiple data sites. The work proposes estimation methods suited to this blockwise-missing, multi-site setting.

shapley-values/explainability/missing-data/research
papersTODAY 04:00 UTC

Diffusion Model Imputes Missing Values in Mixed Numerical and Categorical Data

A new arXiv paper introduces Impute-EM, a diffusion-based approach for filling in missing values in datasets that mix numerical, categorical, and binary variables. Most existing diffusion imputation methods convert discrete variables into continuous stand-ins, which the authors argue is a limitation the native mixed-state design avoids. The work targets heterogeneous data mining settings where such mixed variable types commonly appear together.

diffusion-models/data-imputation/missing-data/research
papersSEP 11 04:00 UTC

SafeImpute Uses Conformal Selection for Clinical Data Imputation

A new arXiv paper introduces SafeImpute, a method for filling in missing laboratory values in clinical datasets where patient visits are irregular and tests are ordered unevenly. The approach applies conformal selection to provide reliability guarantees, rather than only improving average imputation accuracy. It aims to give clinicians more dependable guidance when key lab indicators are absent.

clinical-ai/missing-data/conformal-prediction/research
papersSEP 10 04:00 UTC

arXiv Paper Analyzes Measure Consistency Regularization for Partially Observed Data

A revised arXiv preprint examines a family of regularization techniques designed to handle corrupted data, missing features, and missing modalities in machine learning. The work provides a theoretical analysis of how enforcing consistency between imputed and fully observed data affects learning. It aims to give a more rigorous foundation for methods widely used when training on incomplete inputs.

arxiv/regularization/missing-data/theory