no code implementations • 3 Dec 2019 • Leena Chennuru Vankadara, Siavash Haghiri, Michael Lohaus, Faiz Ul Wahab, Ulrike Von Luxburg
However, there does not exist a fair and thorough assessment of these embedding methods and therefore several key questions remain unanswered: Which algorithms perform better when the embedding dimension is constrained or few triplet comparisons are available?
no code implementations • 25 Sep 2019 • Siavash Haghiri, Leena Chennuru Vankadara, Ulrike Von Luxburg
This problem has been studied in a sub-community of machine learning by the name "Ordinal Embedding".
no code implementations • 21 Aug 2019 • Siavash Haghiri, Felix Wichmann, Ulrike Von Luxburg
We propose to use ordinal embedding methods from machine learning to estimate the scaling function from the relative judgments.
no code implementations • 17 May 2019 • Siavash Haghiri, Patricia Rubisch, Robert Geirhos, Felix Wichmann, Ulrike Von Luxburg
In this paper we study whether the use of comparison-based (ordinal) data, combined with machine learning algorithms, can boost the reliability of crowdsourcing studies for psychophysics, such that they can achieve performance close to a lab experiment.
no code implementations • ICML 2018 • Siavash Haghiri, Damien Garreau, Ulrike Von Luxburg
Assume we are given a set of items from a general metric space, but we neither have access to the representation of the data nor to the distances between data points.
no code implementations • 5 Apr 2017 • Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike Von Luxburg
We consider machine learning in a comparison-based setting where we are given a set of points in a metric space, but we have no access to the actual distances between the points.