170 citations · 316 across the 8 of their papers we have counts for
10 papers
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…
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…
Deep Learning for Survival Analysis: A Review
Simon Wiegrebe, Philipp Kopper, Raphael Sonabend +2
The influx of deep learning (DL) techniques into the field of survival analysis in recent years has led to substantial methodological progress; for instance, learning from unstruct…
When Are Scoring Rules Proper? Bridging Theory and Practice in Survival Model Evaluation
John Zobolas, Raphael Sonabend, Riccardo De Bin +4
Proper scoring rules encourage probabilistic predictions that match the true underlying distribution and are central to model evaluation, with increasing relevance in automated wor…
Flexible Group Fairness Metrics for Survival Analysis
Raphael Sonabend, Florian Pfisterer, Alan Mishler +4
Algorithmic fairness is an increasingly important field concerned with detecting and mitigating biases in machine learning models. There has been a wealth of literature for algorit…
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures
Raphael Sonabend, Andreas Bender, Sebastian Vollmer
In this paper we consider how to evaluate survival distribution predictions with measures of discrimination. This is a non-trivial problem as discrimination measures are the most c…
Designing Machine Learning Toolboxes: Concepts, Principles and Patterns
Franz J. Király, Markus Löning, Anthony Blaom +2
Machine learning (ML) and AI toolboxes such as scikit-learn or Weka are workhorses of contemporary data scientific practice -- their central role being enabled by usable yet powerf…
distr6: R6 Object-Oriented Probability Distributions Interface in R
Raphael Sonabend, Franz Kiraly
distr6 is an object-oriented (OO) probability distributions interface leveraging the extensibility and scalability of R6, and the speed and efficiency of Rcpp. Over 50 probability…
mlr3proba: An R Package for Machine Learning in Survival Analysis
Raphael Sonabend, Franz J. Király, Andreas Bender +2
As machine learning has become increasingly popular over the last few decades, so too has the number of machine learning interfaces for implementing these models. Whilst many R lib…
NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature
Franz J Király, Bilal Mateen, Raphael Sonabend
Objective: To determine the completeness of argumentative steps necessary to conclude effectiveness of an algorithm in a sample of current ML/AI supervised learning literature. Dat…