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Thomas Kneib

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3 papers

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UAI Conference 2025 Conference Paper

Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals

Marcel Arpogaus
Thomas Kneib
Thomas Nagler
David Rügamer

Density regression models allow a comprehensive understanding of data by modeling the complete conditional probability distribution. While flexible estimation approaches such as normalizing flows (NFs) work particularly well in multiple dimensions, interpreting the input-output relationship of such models is often difficult, due to the black-box character of deep learning models. In contrast, existing statistical methods for multivariate outcomes such as multivariate conditional transformation models (MCTMs) are restricted in flexibility and are often not expressive enough to represent complex multivariate probability distributions. In this paper, we combine MCTMs with state-of-the-art and autoregressive NFs to leverage the transparency of MCTMs for modeling interpretable feature effects on the marginal distributions in the first step and the flexibility of neural-network-based NFs techniques to account for complex and non-linear relationships in the joint data distribution. We demonstrate our method’s versatility in various numerical experiments and compare it with MCTMs and other NF models on both simulated and real-world data.

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TMLR Journal 2024 Journal Article

Interpretable Additive Tabular Transformer Networks

Anton Frederik Thielmann
Arik Reuter
Thomas Kneib
David Rügamer
Benjamin Säfken

Attention based Transformer networks have not only revolutionized Natural Language Processing but have also achieved state-of-the-art results for tabular data modeling. The attention mechanism, in particular, has proven to be highly effective in accurately modeling categorical variables. Although deep learning models recently outperform tree-based models, they often lack a complete comprehension of the individual impact of features because of their opaque nature. In contrast, additive neural network structures have proven to be both predictive and interpretable. Within the context of explainable deep learning, we propose Neural Additive Tabular Transformer Networks (NATT), a modeling framework that combines the intelligibility of additive neural networks with the predictive power of Transformer models. NATT offers inherent intelligibility while achieving similar performance to complex deep learning models. To validate its efficacy, we conduct experiments on multiple datasets and find that NATT performs on par with state-of-the-art methods on tabular data and surpasses other interpretable approaches.

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JMLR Journal 2010 Journal Article

Model-based Boosting 2.0

Torsten Hothorn
Peter Bühlmann
Thomas Kneib
Matthias Schmid
Benjamin Hofner

We describe version 2.0 of the R add-on package mboost. The package implements boosting for optimizing general risk functions using component-wise (penalized) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2010. ( edit, beta )

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