most citedImproving post-operative discharge destination prediction of geriatric patients with generative data augmentation

2 citations · 2 across the 3 of their papers we have counts for

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stat.ML2026

Machine Learning in Epidemiology

Marvin N. Wright, Lukas Burk, Pegah Golchian +3

In the age of digital epidemiology, epidemiologists are faced by an increasing amount of data of growing complexity and dimensionality. Machine learning is a set of powerful tools…

stat.ML2025

Imputation Uncertainty in Interpretable Machine Learning Methods

Pegah Golchian, Marvin N. Wright

In real data, missing values occur frequently, which affects the interpretation with interpretable machine learning (IML) methods. Recent work considers bias and shows that model e…

stat.ML2025

Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests

Kristin Blesch, Niklas Koenen, Jan Kapar +4

This paper proposes a method for measuring conditional feature importance via generative modeling. In explainable artificial intelligence (XAI), conditional feature importance asse…