arXiv Machine Learning
Shapley Value Estimation for Multi-Site Data with Blockwise-Missing Features
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Sep 24
Fed-ReMasker: Federated Tabular Imputation under Feature-Level Missingness
Fed-ReMasker is a federated learning approach that adapts the ReMasker masked autoencoder for tabular data imputation, specifically addressing feature-level missingness where entire features are absent at some centers. The method enables centers to impute unobserved features by leveraging knowledge from collaborating institutions. In benchmark tests on synthetic and real-world datasets, Fed-ReMasker achieves the lowest imputation error in the majority of scenarios and remains robust to client heterogeneity, closely matching the performance of a centralized model.
arXiv Machine Learning
Jun 11
Discovery and inference beyond linearity for epidemiological data by integrating Bayesian regression, tree ensembles and Shapley values
arXiv:2505. 00571v3 Announce Type: replace-cross Abstract: Machine Learning (ML) is gaining popularity in epidemiology and healthcare studies for hypothesis-free discovery of risk and protective factors.
arXiv AI
Jun 2
Explainable AI Through a Democratic Lens: DhondtXAI for D'Hondt-Projected Feature Attribution
arXiv:2411. 05196v3 Announce Type: replace Abstract: This study presents DhondtXAI as a SHAP-independent, D'Hondt-based attribution framework for tabular XAI.
arXiv AI
Jul 10
SHIFT: Survival Prediction from Incomplete and Heterogeneous Genomic Data
arXiv:2607. 07725v1 Announce Type: cross Abstract: Genomic prediction models often fail to transfer across institutions because sequencing panels differ across sites, creating structural feature missingness at deployment.
arXiv Machine Learning
2d ago
Missingness-Aware Conformal Prediction Under Cross-Hospital Distribution Shift
The paper introduces a missingness‑aware conformal calibration method for mortality prediction that accounts for cross‑hospital distribution shifts. By selecting a measurement on an independent sample, grouping patients by whether that measurement is recorded, and applying Mondrian calibration within each group, the method avoids reusing calibration outcomes. Experiments on eICU and MIMIC‑IV data show that, compared to pooled calibration, it reduces the worst‑group coverage gap by a median of 1.9 percentage points across six settings, though the benefit varies with predictor and hospital.
arXiv Machine Learning
Jul 8
SafeImpute: Reliable Clinical Data Imputation via Conformal Selection
arXiv:2607. 05613v1 Announce Type: new Abstract: Clinical care often relies on key laboratory indicators, yet real-world patient visits are sparse and tests are ordered irregularly, leading to pervasive missingness.