1 citations · 1 across the 11 of their papers we have counts for
4 papers · 1 filter
Partial Rankings of Optimizers
Julian Rodemann, Hannah Blocher
We introduce a framework for benchmarking optimizers according to multiple criteria over various test functions. Based on a recently introduced union-free generic depth function fo…
Comparing Machine Learning Algorithms by Union-Free Generic Depth
Hannah Blocher, Georg Schollmeyer, Malte Nalenz +1
We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies in linear and metric spaces, there is v…
A note on the connectedness property of union-free generic sets of partial orders
Georg Schollmeyer, Hannah Blocher
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. The connected…
Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms
Hannah Blocher, Georg Schollmeyer, Christoph Jansen +1
We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies of depth functions in linear and metric…