no code implementations • 19 Mar 2024 • Philipp Kopper, David Rügamer, Raphael Sonabend, Bernd Bischl, Andreas Bender
Survival Analysis provides critical insights for partially incomplete time-to-event data in various domains.
1 code implementation • 24 May 2023 • Simon Wiegrebe, Philipp Kopper, Raphael Sonabend, Bernd Bischl, Andreas Bender
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 unstructured or high-dimensional data such as images, text or omics data.
1 code implementation • 10 Dec 2022 • Raphael Sonabend, John Zobolas, Philipp Kopper, Lukas Burk, Andreas Bender
We prove that commonly utilised squared and logarithmic scoring rules that are claimed to be proper are in fact improper, such as the Integrated Survival Brier Score (ISBS).
1 code implementation • 26 May 2022 • Raphael Sonabend, Florian Pfisterer, Alan Mishler, Moritz Schauer, Lukas Burk, Sumantrak Mukherjee, Sebastian Vollmer
In this paper we explore how to utilise existing survival metrics to measure bias with group fairness metrics.
1 code implementation • 9 Dec 2021 • Raphael Sonabend, Andreas Bender, Sebastian Vollmer
In this paper we consider how to evaluate survival distribution predictions with measures of discrimination.
no code implementations • 13 Jan 2021 • Franz J. Király, Markus Löning, Anthony Blaom, Ahmed Guecioueur, Raphael Sonabend
In particular, we develop a conceptual model for the AI/ML domain, with a new type system, called scientific types, at its core.
no code implementations • 18 Aug 2020 • Raphael Sonabend, Franz J. Király, Andreas Bender, Bernd Bischl, Michel Lang
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.
no code implementations • 18 Dec 2018 • 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.