3 papers
stat.ML2025
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…
cs.LG2025
What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models
Jan Kapar, Niklas Koenen, Martin Jullum
Evaluating synthetic tabular data is challenging, since they can differ from the real data in so many ways. There exist numerous metrics of synthetic data quality, ranging from sta…
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…