2 citations · 6 across the 12 of their papers we have counts for
14 papers
Characterizing the Separability Gap in Prior-Data Fitted Networks
Julius Kobialka, Manuel Hülskamp, Thomas Nagler +1
Prior-data fitted networks (PFNs) are transformers pretrained on simulated datasets to approximate Bayesian posterior predictive distributions. Standard PFN theory assumes a separa…
Differentiable Sparsity via -Gating: Simple and Versatile Structured Penalization
Chris Kolb, Laetitia Frost, Bernd Bischl +1
Structured sparsity regularization offers a principled way to compact neural networks, but its non-differentiability breaks compatibility with conventional stochastic gradient desc…
Semi-Implicit Variational Inference via Kernelized Path Gradient Descent
Tobias Pielok, Bernd Bischl, David Rügamer
Semi-implicit variational inference (SIVI) is a powerful framework for approximating complex posterior distributions, but training with the Kullback-Leibler (KL) divergence can be…
Revisiting Unbiased Implicit Variational Inference
Tobias Pielok, Bernd Bischl, David Rügamer
Recent years have witnessed growing interest in semi-implicit variational inference (SIVI) methods due to their ability to rapidly generate samples from complex distributions. Howe…
Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries
Chris Kolb, Tobias Weber, Bernd Bischl +1
Sparse regularization techniques are well-established in machine learning, yet their application in neural networks remains challenging due to the non-differentiability of penaltie…
Scalable Higher-Order Tensor Product Spline Models
David Rügamer
In the current era of vast data and transparent machine learning, it is essential for techniques to operate at a large scale while providing a clear mathematical comprehension of t…
Smoothing the Edges: Smooth Optimization for Sparse Regularization using Hadamard Overparametrization
Chris Kolb, Christian L. Müller, Bernd Bischl +1
We present a framework for smooth optimization of explicitly regularized objectives for (structured) sparsity. These non-smooth and possibly non-convex problems typically rely on s…
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction
Tobias Weber, Michael Ingrisch, Bernd Bischl +1
Undersampling 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 qu…
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis
Tobias Weber, Michael Ingrisch, Bernd Bischl +1
While recent advances in large-scale foundational models show promising results, their application to the medical domain has not yet been explored in detail. In this paper, we prog…
Additive Higher-Order Factorization Machines
David Rügamer
In the age of big data and interpretable machine learning, approaches need to work at scale and at the same time allow for a clear mathematical understanding of the method's inner…
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation
Tobias Weber, Michael Ingrisch, Bernd Bischl +1
The application of deep learning in survival analysis (SA) allows utilizing unstructured and high-dimensional data types uncommon in traditional survival methods. This allows to ad…
Survival-oriented embeddings for improving accessibility to complex data structures
Tobias Weber, Michael Ingrisch, Matthias Fabritius +2
Deep learning excels in the analysis of unstructured data and recent advancements allow to extend these techniques to survival analysis. In the context of clinical radiology, this…
Selective Inference for Additive and Linear Mixed Models
David Rügamer, Philipp F. M. Baumann, Sonja Greven
This work addresses the problem of conducting valid inference for additive and linear mixed models after model selection. One possible solution to overcome overconfident inference…
Selective inference after likelihood- or test-based model selection in linear models
David Rügamer, Sonja Greven
Statistical inference after model selection requires an inference framework that takes the selection into account in order to be valid. Following recent work on selective inference…