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Marvin N. Wright

7 accepted papers

2026

Functional Decomposition and Shapley Interactions for Interpreting Survival Models

Sophie Hanna Langbein, Hubert Baniecki, Fabian Fumagalli, Niklas Koenen, Marvin N. Wright, Julia Herbinger
ICML 2026poster

Hazard and survival functions are natural, interpretable targets in time-to-event prediction, but their inherent non-additivity fundamentally limits standard additive explanation methods. We introduce Survival Functional Decomposition (SurvFD), a principled approach for analyzing feature interaction…

Cited by 0Source ↗Scholar ↗
2025

Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests

Kristin Blesch, Niklas Koenen, Jan Kapar, Pegah Golchian, Lukas Burk, Markus Loecher +1
AAAI 2025technical

This paper proposes a method for measuring conditional feature importance via generative modeling. In explainable artificial intelligence (XAI), conditional feature importance assesses the impact of a feature on a prediction model's performance given the information of other features. Model-agnostic…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗
2025

Fast Estimation of Partial Dependence Functions using Trees

Jinyang Liu, Tessa Steensgaard, Marvin N. Wright, Niklas Pfister, Munir Hiabu
ICML 2025poster

Many existing interpretation methods are based on Partial Dependence (PD) functions that, for a pre-trained machine learning model, capture how a subset of the features affects the predictions by averaging over the remaining features. Notable methods include Shapley additive explanations (SHAP) whic…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Gradient-based Explanations for Deep Learning Survival Models

Sophie Hanna Langbein, Niklas Koenen, Marvin N. Wright
ICML 2025poster

Deep learning survival models often outperform classical methods in time-to-event predictions, particularly in personalized medicine, but their "black box" nature hinders broader adoption. We propose a framework for gradient-based explanation methods tailored to survival neural networks, extending t…

Cited by 0Source ↗PDF ↗Scholar ↗
2023

Adversarial Random Forests for Density Estimation and Generative Modeling

David S. Watson, Kristin Blesch, Jan Kapar, Marvin N. Wright
AISTATS 2023poster

We propose methods for density estimation and data synthesis using a novel form of unsupervised random forests. Inspired by generative adversarial networks, we implement a recursive procedure in which trees gradually learn structural properties of the data through alternating rounds of generation an…

Cited by 30Source ↗PDF ↗code ↗Scholar ↗
2023

Unifying local and global model explanations by functional decomposition of low dimensional structures

Munir Hiabu, Joseph T. Meyer, Marvin N. Wright
AISTATS 2023poster

We consider a global representation of a regression or classification function by decomposing it into the sum of main and interaction components of arbitrary order. We propose a new identification constraint that allows for the extraction of interventional SHAP values and partial dependence plots, t…

Cited by 33Source ↗PDF ↗code ↗Scholar ↗