5 papers
Autoencoding Random Forests
Binh Duc Vu, Jan Kapar, Marvin Wright +1
We propose a principled method for autoencoding with random forests. Our strategy builds on foundational results from nonparametric statistics and spectral graph theory to learn a…
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…
Toward Understanding the Disagreement Problem in Neural Network Feature Attribution
Niklas Koenen, Marvin N. Wright
In recent years, neural networks have demonstrated their remarkable ability to discern intricate patterns and relationships from raw data. However, understanding the inner workings…
CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests
Susanne Dandl, Kristin Blesch, Timo Freiesleben +4
Counterfactual explanations elucidate algorithmic decisions by pointing to scenarios that would have led to an alternative, desired outcome. Giving insight into the model's behavio…
arfpy: A python package for density estimation and generative modeling with adversarial random forests
Kristin Blesch, Marvin N. Wright
This paper introduces , a python implementation of Adversarial Random Forests (ARF) (Watson et al., 2023), which is a lightweight procedure for synthesizing new dat…