Web: http://arxiv.org/abs/2206.07948

June 17, 2022, 1:10 a.m. | Patrick Hemmer, Sebastian Schellhammer, Michael Vössing, Johannes Jakubik, Gerhard Satzger

cs.LG updates on arXiv.org arxiv.org

Machine learning (ML) models are increasingly being used in application
domains that often involve working together with human experts. In this
context, it can be advantageous to defer certain instances to a single human
expert when they are difficult to predict for the ML model. While previous work
has focused on scenarios with one distinct human expert, in many real-world
situations several human experts with varying capabilities may be available. In
this work, we propose an approach that trains a …

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