Zobrazeno 1 - 10
of 85
pro vyhledávání: '"Sonabend, Raphael"'
Open-source software is released under an open-source licence, which means the software can be shared, adapted, and reshared without prejudice. In the context of open-source software, community managers manage the communities that contribute to the d
Externí odkaz:
http://arxiv.org/abs/2407.00345
Autor:
Burk, Lukas, Zobolas, John, Bischl, Bernd, Bender, Andreas, Wright, Marvin N., Sonabend, Raphael
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 methodological research to scientifically compare new and existing mo
Externí odkaz:
http://arxiv.org/abs/2406.04098
Survival Analysis provides critical insights for partially incomplete time-to-event data in various domains. It is also an important example of probabilistic machine learning. The probabilistic nature of the predictions can be exploited by using (pro
Externí odkaz:
http://arxiv.org/abs/2403.13150
This paper extends the FAIR (Findable, Accessible, Interoperable, Reusable) guidelines to provide criteria for assessing if software conforms to best practices in open source. By adding 'USE' (User-Centered, Sustainable, Equitable), software developm
Externí odkaz:
http://arxiv.org/abs/2402.02824
Publikováno v:
Artif Intell Rev 57, 65 (2024)
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. In t
Externí odkaz:
http://arxiv.org/abs/2305.14961
Scoring rules promote rational and honest decision-making, which is becoming increasingly important for automated procedures in `auto-ML'. In this paper we survey common squared and logarithmic scoring rules for survival analysis and determine which
Externí odkaz:
http://arxiv.org/abs/2212.05260
Autor:
Sonabend, Raphael, Pfisterer, Florian, Mishler, Alan, Schauer, Moritz, Burk, Lukas, Mukherjee, Sumantrak, Vollmer, Sebastian
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 algorithmic fairness in regression and classification however there has been
Externí odkaz:
http://arxiv.org/abs/2206.03256
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 commonly used in survival analysis and yet there is no clear method to
Externí odkaz:
http://arxiv.org/abs/2112.04828
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 powerful designs that allow to easily specify, train and validate complex mo
Externí odkaz:
http://arxiv.org/abs/2101.04938
Autor:
Sonabend, Raphael, Kiraly, Franz
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 distributions are currently implemented in the package with `core' met
Externí odkaz:
http://arxiv.org/abs/2009.02993