1.3k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Ludwig-Maximilians-Universität MünchenDE63 papers
- Technical University of MunichDE41 papers
- LMU KlinikumDE29 papers
- Imperial College LondonGB8 papers
- Helmholtz MunichDE6 papers
- TUM KlinikumDE6 papers
- Zimmer Biomet (Germany)DE6 papers
- Fraunhofer Institute for Integrated CircuitsDE4 papers
- Munich School of PhilosophyDE4 papers
- TU Dortmund UniversityDE4 papers
- University College LondonGB4 papers
- Zimmer Biomet (Netherlands)NL4 papers
22 papers · 1 filter
Uncertainty Quantification in Pairwise Difference Learning for Classification
Mohamed Karim Belaid, Maximilian Rabus, Eyke Hüllermeier
Instead of learning a mapping from instances to outcomes in the standard way, the key idea of pairwise difference learning (PDL) is to learn a function that takes two instances as…
MORE-PLR: multi-output regression employed for partial label ranking
Santo M. A. R. Thies, Juan C. Alfaro, Viktor Bengs
The partial label ranking problem is a supervised learning scenario that aims to fit a preference model that predicts a bucket order defined over a set of labels for a given input…
bde: A Python Package for Bayesian Deep Ensembles via MILE
Vyron Arvanitis, Angelos Aslanidis, Emanuel Sommer +1
bde is a user-friendly Python package for Bayesian Deep Ensembles with a particular focus on tabular data. Built on an efficient JAX implementation of the sampling-based inference…
Distribution Matching for Graph Quantification Under Structural Covariate Shift
Clemens Damke, Eyke Hüllermeier
Graphs are commonly used in machine learning to model relationships between instances. Consider the task of predicting the political preferences of users in a social network; to so…
UST-GNN: A Unified Spatial--Topological Graph Neural Network Framework for Urban Analytics--Demonstrated through a Case Study on Urban Health Prediction
Minwei Zhao, Sanja Scepanovic, Stephen Law +3
Understanding how social, demographic, environmental, and spatial factors jointly shape urban outcomes is essential for sustainable urban development and evidence-based policy. Tra…
Causal machine learning for predicting treatment outcomes
Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk +7
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of…
CUQ-GNN: Committee-based Graph Uncertainty Quantification using Posterior Networks
Clemens Damke, Eyke Hüllermeier
In this work, we study the influence of domain-specific characteristics when defining a meaningful notion of predictive uncertainty on graph data. Previously, the so-called Graph P…
Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance
Thomas Decker, Alexander Koebler, Michael Lebacher +3
Monitoring and maintaining machine learning models are among the most critical challenges in translating recent advances in the field into real-world applications. However, current…
On the Robustness of Global Feature Effect Explanations
Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl +1
We study the robustness of global post-hoc explanations for predictive models trained on tabular data. Effects of predictor features in black-box supervised learning are an essenti…
Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles
Leonardo Arrighi, Luca Pennella, Gabriel Marques Tavares +1
Understanding the decisions of tree-based ensembles and their relationships is pivotal for machine learning model interpretation. Recent attempts to mitigate the human-in-the-loop…
Lazy Data Practices Harm Fairness Research
Jan Simson, Alessandro Fabris, Christoph Kern
Data practices shape research and practice on fairness in machine learning (fair ML). Critical data studies offer important reflections and critiques for the responsible advancemen…
Interpretable Machine Learning for TabPFN
David Rundel, Julius Kobialka, Constantin von Crailsheim +3
The recently developed Prior-Data Fitted Networks (PFNs) have shown very promising results for applications in low-data regimes. The TabPFN model, a special case of PFNs for tabula…
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification
Sandra Gilhuber, Julian Busch, Daniel Rotthues +2
Node classification is one of the core tasks on attributed graphs, but successful graph learning solutions require sufficiently labeled data. To keep annotation costs low, active g…
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…
iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios
Maximilian Muschalik, Fabian Fumagalli, Rohit Jagtani +2
Post-hoc explanation techniques such as the well-established partial dependence plot (PDP), which investigates feature dependencies, are used in explainable artificial intelligence…
Investigating Labeler Bias in Face Annotation for Machine Learning
Luke Haliburton, Sinksar Ghebremedhin, Robin Welsch +2
In a world increasingly reliant on artificial intelligence, it is more important than ever to consider the ethical implications of artificial intelligence on humanity. One key unde…
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives
Matthias Feurer, Katharina Eggensperger, Edward Bergman +3
Modern machine learning models are often constructed taking into account multiple objectives, e.g., minimizing inference time while also maximizing accuracy. Multi-objective hyperp…
Symbolic Recovery of Differential Equations: The Identifiability Problem
Philipp Scholl, Aras Bacho, Holger Boche +1
Symbolic recovery of differential equations is the ambitious attempt at automating the derivation of governing equations with the use of machine learning techniques. In contrast to…
Locating disparities in machine learning
Moritz von Zahn, Oliver Hinz, Stefan Feuerriegel
Machine learning can provide predictions with disparate outcomes, in which subgroups of the population (e.g., defined by age, gender, or other sensitive attributes) are systematica…
Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview
Florian Karl, Tobias Pielok, Julia Moosbauer +10
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows. This arises from the fact that machine learning methods and corresponding preproc…
Experimental Standards for Deep Learning in Natural Language Processing Research
Dennis Ulmer, Elisa Bassignana, Max Müller-Eberstein +5
The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well. Yet, compared to more…
Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition
Felix Ott, David Rügamer, Lucas Heublein +2
Cross-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. Cr…