Marvin N. Wright

According to our database, Marvin N. Wright authored at least 28 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Interpreting Deep Neural Networks with the Package innsight.

Niklas Koenen, Marvin N. Wright

J. Stat. Softw., 2024

Fast Estimation of Partial Dependence Functions using Trees.

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

CoRR, 2024

A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data.

Lukas Burk, John Zobolas, Bernd Bischl, Andreas Bender, Marvin N. Wright, Raphael Sonabend

CoRR, 2024

Interpretable Machine Learning for Survival Analysis.

Sophie Hanna Langbein, Mateusz Krzyzinski, Mikolaj Spytek, Hubert Baniecki, Przemyslaw Biecek, Marvin N. Wright

CoRR, 2024

Toward Understanding the Disagreement Problem in Neural Network Feature Attribution.

Niklas Koenen, Marvin N. Wright

Proceedings of the Explainable Artificial Intelligence, 2024

A Guide to Feature Importance Methods for Scientific Inference.

Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright, Bernd Bischl, Giuseppe Casalicchio, Gunnar König

Proceedings of the Explainable Artificial Intelligence, 2024

CountARFactuals - Generating Plausible Model-Agnostic Counterfactual Explanations with Adversarial Random Forests.

Susanne Dandl, Kristin Blesch, Timo Freiesleben, Gunnar König, Jan Kapar, Bernd Bischl, Marvin N. Wright

Proceedings of the Explainable Artificial Intelligence, 2024

2023

survex: an R package for explaining machine learning survival models.

Mikolaj Spytek, Mateusz Krzyzinski, Sophie Hanna Langbein, Hubert Baniecki, Marvin N. Wright, Przemyslaw Biecek

Bioinform., December, 2023

arfpy: A python package for density estimation and generative modeling with adversarial random forests.

Kristin Blesch, Marvin N. Wright

CoRR, 2023

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process.

Christoph Molnar, Timo Freiesleben, Gunnar König, Julia Herbinger, Tim Reisinger, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl

Proceedings of the Explainable Artificial Intelligence, 2023

Unfooling SHAP and SAGE: Knockoff Imputation for Shapley Values.

Kristin Blesch, Marvin N. Wright, David S. Watson

Proceedings of the Explainable Artificial Intelligence, 2023

Adversarial Random Forests for Density Estimation and Generative Modeling.

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

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

Munir Hiabu, Joseph T. Meyer, Marvin N. Wright

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Conditional Feature Importance for Mixed Data.

Kristin Blesch, David S. Watson, Marvin N. Wright

CoRR, 2022

Smooth densities and generative modeling with unsupervised random forests.

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

CoRR, 2022

Are SHAP Values Biased Towards High-Entropy Features?

Raphael Baudeu, Marvin N. Wright, Markus Loecher

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022

2021

Testing conditional independence in supervised learning algorithms.

David S. Watson, Marvin N. Wright

Mach. Learn., 2021

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process.

Christoph Molnar, Timo Freiesleben, Gunnar König, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl

CoRR, 2021

Generalization of the Change of Variables Formula with Applications to Residual Flows.

Niklas Koenen, Marvin N. Wright, Peter Maaß, Jens Behrmann

CoRR, 2021

2020

Discrete-time survival forests with Hellinger distance decision trees.

Matthias Schmid, Thomas Welchowski, Marvin N. Wright, Moritz Berger

Data Min. Knowl. Discov., 2020

2019

Hyperparameters and tuning strategies for random forest.

Philipp Probst, Marvin N. Wright, Anne-Laure Boulesteix

WIREs Data Mining Knowl. Discov., 2019

Testing Conditional Predictive Independence in Supervised Learning Algorithms.

David S. Watson, Marvin N. Wright

CoRR, 2019

Block Forests: random forests for blocks of clinical and omics covariate data.

Roman Hornung, Marvin N. Wright

BMC Bioinform., 2019

2018

Support Vector Machines for Survival Analysis with R.

Césaire J. K. Fouodo, Inke R. König, Claus Weihs, Andreas Ziegler, Marvin N. Wright

R J., 2018

The revival of the Gini importance?

Stefano Nembrini, Inke R. König, Marvin N. Wright

Bioinform., 2018

2016

On the use of Harrell's C for clinical risk prediction via random survival forests.

Matthias Schmid, Marvin N. Wright, Andreas Ziegler

Expert Syst. Appl., 2016

Random forests for survival analysis using maximally selected rank statistics.

Marvin N. Wright, Theresa Dankowski, Andreas Ziegler

CoRR, 2016

Do little interactions get lost in dark random forests?

Marvin N. Wright, Andreas Ziegler, Inke R. König

BMC Bioinform., 2016