7 citations · 17 across the 4 of their papers we have counts for
4 papers
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks
Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi +2
We show that deep networks trained to satisfy demographic parity often do so through a form of race or gender awareness, and that the more we force a network to be fair, the more a…
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers
Dominik Zietlow, Michael Lohaus, Guha Balakrishnan +4
Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algori…
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation
Leena Chennuru Vankadara, Siavash Haghiri, Michael Lohaus +2
The objective of ordinal embedding is to find a Euclidean representation of a set of abstract items, using only answers to triplet comparisons of the form "Is item closer to th…
Uncertainty Estimates for Ordinal Embeddings
Michael Lohaus, Philipp Hennig, Ulrike von Luxburg
To investigate objects without a describable notion of distance, one can gather ordinal information by asking triplet comparisons of the form "Is object closer to or is …