4 citations · 4 across the 6 of their papers we have counts for
8 papers
Multimodal synthesis of MRI and tabular data with diffusion in a joint latent space via cross-attention
Daniel Mensing, Jan Kapar, Jochen G. Hirsch +3
We propose a multimodal latent diffusion model that jointly synthesizes volumetric magnetic resonance imaging (MRI) and tabular clinical data within a shared latent space via cross…
Machine Learning in Epidemiology
Marvin N. Wright, Lukas Burk, Pegah Golchian +3
In the age of digital epidemiology, epidemiologists are faced by an increasing amount of data of growing complexity and dimensionality. Machine learning is a set of powerful tools…
Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests
Jan Kapar, Kathrin Günther, Lori Ann Vallis +27
Synthetic data hold substantial potential to address practical challenges in epidemiology due to restricted data access and privacy concerns. However, many current methods suffer f…
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…
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…
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…
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…
Adversarial random forests for density estimation and generative modeling
David S. Watson, Kristin Blesch, Jan Kapar +1
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 recursiv…