Search Results for author: Matthaeus Kleindessner

Found 4 papers, 1 papers with code

Evaluating the Fairness of Discriminative Foundation Models in Computer Vision

no code implementations18 Oct 2023 Junaid Ali, Matthaeus Kleindessner, Florian Wenzel, Kailash Budhathoki, Volkan Cevher, Chris Russell

We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP), that are used for labeling tasks.

Fairness Image Captioning +2

Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations

1 code implementation11 Jul 2022 Andrii Zadaianchuk, Matthaeus Kleindessner, Yi Zhu, Francesco Locatello, Thomas Brox

In this paper, we show that recent advances in self-supervised feature learning enable unsupervised object discovery and semantic segmentation with a performance that matches the state of the field on supervised semantic segmentation 10 years ago.

Clustering Object +3

Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information

no code implementations16 Feb 2021 Pranjal Awasthi, Alex Beutel, Matthaeus Kleindessner, Jamie Morgenstern, Xuezhi Wang

An alternate approach that is commonly used is to separately train an attribute classifier on data with sensitive attribute information, and then use it later in the ML pipeline to evaluate the bias of a given classifier.

Attribute BIG-bench Machine Learning +2

Crowdsourcing with Arbitrary Adversaries

no code implementations ICML 2018 Matthaeus Kleindessner, Pranjal Awasthi

Most existing works on crowdsourcing assume that the workers follow the Dawid-Skene model, or the one-coin model as its special case, where every worker makes mistakes independently of other workers and with the same error probability for every task.

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