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missing data

topic4 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
papersSEP 12 04:00 UTC

Paper Proposes Reconstruction Method for Multimodal Sentiment Analysis with Missing Data

A new arXiv paper addresses multimodal sentiment analysis when some input modalities are missing at inference time. The authors note that text-centric fusion methods, which lean on the sentiment signal in text, tend to lose accuracy under such conditions. Their approach uses semantic-aware completeness-based reconstruction to compensate for incomplete inputs.

multimodal/sentiment-analysis/research/robustness
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