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Marvin N. Wright
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2020 – today
- 2024
- [c8]Susanne Dandl
, Kristin Blesch
CountARFactuals - Generating Plausible Model-Agnostic Counterfactual Explanations with Adversarial Random Forests. xAI (3) 2024: 85-107 - [c7]Niklas Koenen
Toward Understanding the Disagreement Problem in Neural Network Feature Attribution. xAI (3) 2024: 247-269 - [c6]Fiona Katharina Ewald
A Guide to Feature Importance Methods for Scientific Inference. xAI (2) 2024: 440-464 - [i17]Sophie Hanna Langbein, Mateusz Krzyzinski, Mikolaj Spytek, Hubert Baniecki, Przemyslaw Biecek, Marvin N. Wright:
Interpretable Machine Learning for Survival Analysis. CoRR abs/2403.10250 (2024) - [i16]Susanne Dandl, Kristin Blesch, Timo Freiesleben, Gunnar König, Jan Kapar, Bernd Bischl, Marvin N. Wright:
CountARFactuals - Generating plausible model-agnostic counterfactual explanations with adversarial random forests. CoRR abs/2404.03506 (2024) - [i15]Niklas Koenen, Marvin N. Wright:
Toward Understanding the Disagreement Problem in Neural Network Feature Attribution. CoRR abs/2404.11330 (2024) - [i14]Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright, Bernd Bischl, Giuseppe Casalicchio, Gunnar König:
A Guide to Feature Importance Methods for Scientific Inference. CoRR abs/2404.12862 (2024) - [i13]Lukas Burk
A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data. CoRR abs/2406.04098 (2024) - [i12]Jinyang Liu, Tessa Steensgaard, Marvin N. Wright, Niklas Pfister, Munir Hiabu:
Fast Estimation of Partial Dependence Functions using Trees. CoRR abs/2410.13448 (2024) - 2023
- [j9]Mikolaj Spytek, Mateusz Krzyzinski
survex: an R package for explaining machine learning survival models. Bioinform. 39(12) (2023) - [c5]David S. Watson, Kristin Blesch, Jan Kapar, Marvin N. Wright
Adversarial Random Forests for Density Estimation and Generative Modeling. AISTATS 2023: 5357-5375 - [c4]Munir Hiabu, Joseph T. Meyer, Marvin N. Wright
Unifying local and global model explanations by functional decomposition of low dimensional structures. AISTATS 2023: 7040-7060 - [c3]Kristin Blesch
Unfooling SHAP and SAGE: Knockoff Imputation for Shapley Values. xAI (1) 2023: 131-146 - [c2]Christoph Molnar
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. xAI (1) 2023: 456-479 - [i11]Niklas Koenen, Marvin N. Wright:
Interpreting Deep Neural Networks with the Package innsight. CoRR abs/2306.10822 (2023) - [i10]Mikolaj Spytek, Mateusz Krzyzinski, Sophie Hanna Langbein, Hubert Baniecki, Marvin N. Wright, Przemyslaw Biecek:
survex: an R package for explaining machine learning survival models. CoRR abs/2308.16113 (2023) - [i9]Kristin Blesch, Marvin N. Wright:
arfpy: A python package for density estimation and generative modeling with adversarial random forests. CoRR abs/2311.07366 (2023) - 2022
- [c1]Raphael Baudeu
Are SHAP Values Biased Towards High-Entropy Features? PKDD/ECML Workshops (1) 2022: 418-433 - [i8]David S. Watson, Kristin Blesch, Jan Kapar, Marvin N. Wright
Smooth densities and generative modeling with unsupervised random forests. CoRR abs/2205.09435 (2022) - [i7]Munir Hiabu, Joseph T. Meyer, Marvin N. Wright
Unifying local and global model explanations by functional decomposition of low dimensional structures. CoRR abs/2208.06151 (2022) - [i6]Kristin Blesch, David S. Watson, Marvin N. Wright
Conditional Feature Importance for Mixed Data. CoRR abs/2210.03047 (2022) - 2021
- [j8]David S. Watson
Testing conditional independence in supervised learning algorithms. Mach. Learn. 110(8): 2107-2129 (2021) - [i5]Niklas Koenen, Marvin N. Wright, Peter Maaß, Jens Behrmann:
Generalization of the Change of Variables Formula with Applications to Residual Flows. CoRR abs/2107.04346 (2021) - [i4]Christoph Molnar, Timo Freiesleben, Gunnar König, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl:
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. CoRR abs/2109.01433 (2021) - 2020
- [j7]Matthias Schmid
Discrete-time survival forests with Hellinger distance decision trees. Data Min. Knowl. Discov. 34(3): 812-832 (2020)
2010 – 2019
- 2019
- [j6]Roman Hornung
Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinform. 20(1): 358:1-358:17 (2019) - [j5]Philipp Probst, Marvin N. Wright
Hyperparameters and tuning strategies for random forest. WIREs Data Mining Knowl. Discov. 9(3) (2019) - [i3]David S. Watson, Marvin N. Wright:
Testing Conditional Predictive Independence in Supervised Learning Algorithms. CoRR abs/1901.09917 (2019) - 2018
- [j4]Stefano Nembrini, Inke R. König, Marvin N. Wright
The revival of the Gini importance? Bioinform. 34(21): 3711-3718 (2018) - [j3]Césaire J. K. Fouodo, Inke R. König, Claus Weihs, Andreas Ziegler, Marvin N. Wright
Support Vector Machines for Survival Analysis with R. R J. 10(1): 412 (2018) - [i2]Philipp Probst, Marvin N. Wright, Anne-Laure Boulesteix:
Hyperparameters and Tuning Strategies for Random Forest. CoRR abs/1804.03515 (2018) - 2016
- [j2]Marvin N. Wright
Do little interactions get lost in dark random forests? BMC Bioinform. 17: 145 (2016) - [j1]Matthias Schmid
On the use of Harrell's C for clinical risk prediction via random survival forests. Expert Syst. Appl. 63: 450-459 (2016) - [i1]Marvin N. Wright, Theresa Dankowski, Andreas Ziegler:
Random forests for survival analysis using maximally selected rank statistics. CoRR abs/1605.03391 (2016)
Coauthor Index
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