Search Results for author: Michael Lohaus

Found 5 papers, 1 papers with code

Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks

no code implementations9 Apr 2022 Michael Lohaus, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, Chris Russell

First, we train a two-headed network to predict the protected attribute (such as race or gender) alongside the original task, and second, we enforce demographic parity by taking a weighted sum of the heads.

Fairness

Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers

no code implementations CVPR 2022 Dominik Zietlow, Michael Lohaus, Guha Balakrishnan, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Chris Russell

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 algorithm would otherwise be less accurate.

Fairness

Insights into Ordinal Embedding Algorithms: A Systematic Evaluation

no code implementations3 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?

Representation Learning

Uncertainty Estimates for Ordinal Embeddings

no code implementations27 Jun 2019 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 $x$ closer to $y$ or is $x$ closer to $z$?"

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