36 citations · 173 across the 61 of their papers we have counts for
6 papers · 1 filter
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…