36 citations · 173 across the 61 of their papers we have counts for
16 papers · 1 filter
ConstraintMatch for Semi-constrained Clustering
Jann Goschenhofer, Bernd Bischl, Zsolt Kira
Constrained clustering allows the training of classification models using pairwise constraints only, which are weak and relatively easy to mine, while still yielding full-supervisi…
Post-hoc Orthogonalization for Mitigation of Protected Feature Bias in CXR Embeddings
Tobias Weber, Michael Ingrisch, Bernd Bischl +1
Purpose: To analyze and remove protected feature effects in chest radiograph embeddings of deep learning models. Methods: An orthogonalization is utilized to remove the influence o…
Evaluating machine learning models in non-standard settings: An overview and new findings
Roman Hornung, Malte Nalenz, Lennart Schneider +5
Estimating the generalization error (GE) of machine learning models is fundamental, with resampling methods being the most common approach. However, in non-standard settings, parti…
AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments
Yang Zhang, Yawei Li, Hannah Brown +5
Feature attribution explains neural network outputs by identifying relevant input features. The attribution has to be faithful, meaning that the attributed features must mirror the…
Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning
Amirhossein Vahidi, Lisa Wimmer, Hüseyin Anil Gündüz +3
Ensembling a neural network is a widely recognized approach to enhance model performance, estimate uncertainty, and improve robustness in deep supervised learning. However, deep en…
A Dual-Perspective Approach to Evaluating Feature Attribution Methods
Yawei Li, Yang Zhang, Kenji Kawaguchi +3
Feature attribution methods attempt to explain neural network predictions by identifying relevant features. However, establishing a cohesive framework for assessing feature attribu…
How Different Is Stereotypical Bias Across Languages?
Ibrahim Tolga Öztürk, Rostislav Nedelchev, Christian Heumann +4
Recent studies have demonstrated how to assess the stereotypical bias in pre-trained English language models. In this work, we extend this branch of research in multiple different…
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…
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers
Lukas Rauch, Matthias Aßenmacher, Denis Huseljic +3
Deep active learning (DAL) seeks to reduce annotation costs by enabling the model to actively query instance annotations from which it expects to learn the most. Despite extensive…
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations
Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl +1
This work introduces interpretable regional descriptors, or IRDs, for local, model-agnostic interpretations. IRDs are hyperboxes that describe how an observation's feature values c…
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…
Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning
Daniel Saggau, Mina Rezaei, Bernd Bischl +1
Learning quality document embeddings is a fundamental problem in natural language processing (NLP), information retrieval (IR), recommendation systems, and search engines. Despite…
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou +3
Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approach…
Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments
Felix Ott, Lucas Heublein, David Rügamer +2
The localization of objects is a crucial task in various applications such as robotics, virtual and augmented reality, and the transportation of goods in warehouses. Recent advance…
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…
Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition
Felix Ott, David Rügamer, Lucas Heublein +2
The performance of a machine learning model degrades when it is applied to data from a similar but different domain than the data it has initially been trained on. The goal of doma…