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Found 6 papers, 3 papers with code
Partial Rankings of Optimizers
We introduce a framework for benchmarking optimizers according to multiple criteria over various test functions.
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.
A note on the connectedness property of union-free generic sets of partial orders
This short note describes and proves a connectedness property which was introduced in Blocher et al. [2023] in the context of data depth functions for partial orders.
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.
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.
