36 citations · 173 across the 61 of their papers we have counts for
63 papers
mlr3torch: A Deep Learning Framework in R based on mlr3 and torch
Sebastian Fischer, Lukas Burk, Carson Zhang +2
Deep learning (DL) has become a cornerstone of modern machine learning (ML) praxis. We introduce the R package mlr3torch, which is an extensible DL framework for the mlr3 ecosystem…
Analyzing Error Sources in Global Feature Effect Estimation
Timo Heiß, Coco Bögel, Bernd Bischl +1
Global feature effects such as partial dependence (PD) and accumulated local effects (ALE) plots are widely used to interpret black-box models. However, they are only estimates of…
CASHomon Sets: Efficient Rashomon Sets Across Multiple Model Classes and their Hyperparameters
Fiona Katharina Ewald, Martin Binder, Matthias Feurer +2
Rashomon sets are model sets within one model class that perform nearly as well as a reference model from the same model class. They reveal the existence of alternative well-perfor…
xplainfi: Feature Importance and Statistical Inference for Machine Learning in R
Lukas Burk, Fiona Katharina Ewald, Giuseppe Casalicchio +2
We introduce xplainfi, an R package built on top of the mlr3 ecosystem for global, loss-based feature importance methods for machine learning models. Various feature importance met…
Structured Credal Learning
Varun Venkatesh, Eyke Hüllermeier, Bernd Bischl +1
Real-world learning tasks often encounter uncertainty due to covariate shift and noisy or inconsistent labels. However, existing robust learning methods merge these effects into a…
mlr3mbo: Bayesian Optimization in R
Marc Becker, Lennart Schneider, Martin Binder +2
We present mlr3mbo, a modular toolbox for Bayesian optimization in R. mlr3mbo supports single- and multi-objective optimization, multi-point proposals, batch and asynchronous paral…
Optimal Transport Group Counterfactual Explanations
Enrique Valero-Leal, Bernd Bischl, Pedro Larrañaga +2
Group counterfactual explanations find a set of counterfactual instances to explain a group of input instances contrastively. However, existing methods either (i) optimize counterf…
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…
TABFAIRGDT: A Fast Fair Tabular Data Generator using Autoregressive Decision Trees
Emmanouil Panagiotou, Benoît Ronval, Arjun Roy +4
Ensuring fairness in machine learning remains a significant challenge, as models often inherit biases from their training data. Generative models have recently emerged as a promisi…
Revisiting Active Learning under (Human) Label Variation
Cornelia Gruber, Helen Alber, Bernd Bischl +3
Access to high-quality labeled data remains a limiting factor in applied supervised learning. While label variation (LV), i.e., differing labels for the same instance, is common, e…
Overtuning in Hyperparameter Optimization
Lennart Schneider, Bernd Bischl, Matthias Feurer
Hyperparameter optimization (HPO) aims to identify an optimal hyperparameter configuration (HPC) such that the resulting model generalizes well to unseen data. As the expected gene…
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…
Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learning
Lisa Wimmer, Bernd Bischl, Ludwig Bothmann
The correct way to quantify predictive uncertainty in neural networks remains a topic of active discussion. In particular, it is unclear whether the state-of-the art entropy decomp…
Calibrating LLMs with Information-Theoretic Evidential Deep Learning
Yawei Li, David Rügamer, Bernd Bischl +1
Fine-tuned large language models (LLMs) often exhibit overconfidence, particularly when trained on small datasets, resulting in poor calibration and inaccurate uncertainty estimate…
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…
Privilege Scores
Ludwig Bothmann, Philip A. Boustani, Jose M. Alvarez +3
Bias-transforming methods of fairness-aware machine learning aim to correct a non-neutral status quo with respect to a protected attribute (PA). Current methods, however, lack an e…
Constructing Confidence Intervals for 'the' Generalization Error -- a Comprehensive Benchmark Study
Hannah Schulz-Kümpel, Sebastian Fischer, Roman Hornung +3
When assessing the quality of prediction models in machine learning, confidence intervals (CIs) for the generalization error, which measures predictive performance, are a crucial t…
Position: A Call to Action for a Human-Centered AutoML Paradigm
Marius Lindauer, Florian Karl, Anne Klier +6
Automated machine learning (AutoML) was formed around the fundamental objectives of automatically and efficiently configuring machine learning (ML) workflows, aiding the research o…
A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data
Lukas Burk, John Zobolas, Bernd Bischl +3
This work presents the first large-scale neutral benchmark experiment focused on single-event, right-censored, low-dimensional survival data. Benchmark experiments are essential in…
Position: Why We Must Rethink Empirical Research in Machine Learning
Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger +7
We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to un…
FinerCut: Finer-grained Interpretable Layer Pruning for Large Language Models
Yang Zhang, Yawei Li, Xinpeng Wang +5
Overparametrized transformer networks are the state-of-the-art architecture for Large Language Models (LLMs). However, such models contain billions of parameters making large compu…
On Training Survival Models with Scoring Rules
Philipp Kopper, David Rügamer, Raphael Sonabend +2
Scoring rules are an established way of comparing predictive performances across model classes. In the context of survival analysis, they require adaptation in order to accommodate…
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…