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Found 8 papers, 4 papers with code
Joining Forces for Pathology Diagnostics with AI Assistance: The EMPAIA Initiative
Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially.
Comparing Machine Learning Algorithms by Union-Free Generic Depth
We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions.
Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement
Spaces with locally varying scale of measurement, like multidimensional structures with differently scaled dimensions, are pretty common in statistics and machine learning.
Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms
We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions.
In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised Learning
As a practical proof of concept, we spotlight the application of three of our robust extensions on simulated and real-world data.
Multi-Target Decision Making under Conditions of Severe Uncertainty
The quality of consequences in a decision making problem under (severe) uncertainty must often be compared among different targets (goals, objectives) simultaneously.
Statistical Comparisons of Classifiers by Generalized Stochastic Dominance
This yields indeed a powerful framework for the statistical comparison of classifiers over multiple data sets with respect to multiple quality criteria simultaneously.
Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty
The first approach directly utilizes the collected ranking data for obtaining the ordinal part of the preferences, while their cardinal part is constructed implicitly by measuring meta data on the decision maker's consideration times.
