Feel free to get in touch via Mail or LinkedIn!

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Published on October 01, 2024 | 0 min read

Authors: David Rügamer, Bernard X.W. Liew, Zainab Altai, Almond Stöcker Abstract: Semi-structured networks (SSNs) merge the structures familiar from additive models with deep neural networks, allowing the modeling o...

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Published on September 25, 2024 | 0 min read

Accepted papers to the Fast-Track of the 6th Symposium on Advances in Approximate Bayesian Inference: Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? (ICML 2024) with Emanuel and Lisa Bay...

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Published on May 27, 2024 | 0 min read

Authors: Anton Frederik Thielmann, Arik Reuter, Thomas Kneib, David Rügamer, Benjamin Säfken Title: Interpretable Additive Tabular Transformer Networks Abstract: Attention based Transformer networks have not only revolutionized Natural Language Processing but have...

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Published on May 14, 2024 | 0 min read

Accepted papers: Generalizing Orthogonalization for Models with Non-linearities with Chris Kolb and Tobias Weber Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI Position Paper: Rethinking Empirical Research in Mach...

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Published on May 02, 2024 | 0 min read

Our paper UAI Paper How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression was accepted at this year’s UAI.We will upload the paper to Arxiv within the next few days!

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Published on April 26, 2024 | 0 min read

Our paper ECML-PKDD Paper Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry was accepted to be presented at IJCAI’s Sister Track for Best Research Papers.AbstractBayesian inference in deep neural networks is challengin...

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Published on April 22, 2024 | 1 min read

Our paper Interpretable Machine Learning for TabPFN was accepted at the XAI-2024.AbstractThe recently developed Prior-Data Fitted Networks (PFNs) have shown very promising results for applications in low-data regimes. The TabPFN model, a special case ...

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Published on April 08, 2024 | 1 min read

Our paper Estimating Conditional Distributions with Neural Networks using R Package deeptrafo was accepted at the Journal of Statistical Software.AbstractContemporary empirical applications frequently require flexible regression models for complex res...

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Published on April 02, 2024 | 1 min read

Our papers Bayesian Semi-structured Subspace Inference by Daniel Dold, David Rügamer, Beate Sick and Oliver Dürr as well as David’s paper on Scalable Higher-Order Tensor Product Spline Models were accepted at this year’s AIStats.Abstract P...

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Published on January 20, 2024 | 2 min read

For our next project we got around 49.5k Euros funding from the LMU!

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Published on December 05, 2023 | 0 min read

Feel free to get in touch via Mail or LinkedIn!

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Published on December 01, 2023 | 0 min read

New preprint on Unreading Race: Purging Protected Features from Chest X-ray EmbeddingsAbstract Purpose: To analyze and remove protected feature effects in chest radiograph embeddings of deep learning models. Materials and Methods: An...

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Published on November 03, 2023 | 1 min read

Our paper Mixture of Experts Distributional Regression: Implementation Using Robust Estimation with Adaptive First-order Methods was accepted for publication at AStA Advances in Statistical Analysis in the special issue Bridging the gap between AI and Statistics.<h3 id=...

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Published on October 24, 2023 | 0 min read

Daniel’s paper Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models was accepted for publication in Statistics and Computing.AbstractVarious privacy-preserving frameworks that respect the individ...

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Published on October 02, 2023 | 1 min read

Want to learn more about our newest group member?Check out his personal website here!

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Published on October 01, 2023 | 0 min read

Among 196 accepted papers, Gregor and Lisa’s paper Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry was selected to be the best paper at ECML-PKDD 2023 in the Research Track!! They will present our work in the award session in Turin on Sept. 19....

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Published on August 30, 2023 | 1 min read

AbstractCross-modal representation learning learns a shared embedding between two or more modalities to improve performance in a given task compared to using only one of the modalities. Cross-modal representation learning from different data types – such as images and ...

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Published on August 28, 2023 | 1 min read

AbstractUndersampling is a common method in Magnetic Resonance Imaging (MRI) to subsample the number of data points in k-space, reducing acquisition times at the cost of decreased image quality. A popular approach is to employ undersampling patterns following various s...

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Published on August 15, 2023 | 1 min read

We convinced the LMU Munich of our Machine Learning Consulting Unit and received our first grant money!

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Published on August 04, 2023 | 0 min read

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Published on July 20, 2023 | 0 min read

AbstractThis paper presents a framework for smooth optimization of objectives with ℓq and ℓp,q regularization for (structured) sparsity. Finding solutions to these non-smooth and possibly non-convex problems typically relies on specialized optimization routines. In con...

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Published on July 07, 2023 | 1 min read

AbstractBayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approaches asymptotically recover the true posterior but are considered prohibitively exp...

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Published on June 06, 2023 | 1 min read

Theresa’s paper A comprehensive machine learning benchmark study for radiomics-based survival analysis of CT imaging data in patients with hepatic metastases of CRC got accepted at Investigative Radiology!Available ...

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Published on May 28, 2023 | 2 min read

David’s paper A New PHO-rmula for Improved Performance of Semi-Structured Networks got accepted at ICML!Available here.AbstractRecent advances to combine stru...

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Published on April 25, 2023 | 0 min read

Available here.AbstractMultivariate time series measurements in plasma diagnostics present several challenges when training machine learning models: the availability of only a few labeled...

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Published on April 13, 2023 | 0 min read

Our group’s homepage is now online!

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Published on April 01, 2023 | 0 min read