  Discovering reliable measures that inform on autism spectrum disorder (ASD)
diagnosis is critical for providing appropriate and timely treatment for this
neurodevelopmental disorder. In this work we present applications of
adversarial linear factor models in the creation of improved biomarkers for ASD
diagnosis. First, we demonstrate that an adversarial linear factor model can be
used to remove confounding information from our biomarkers, ensuring that they
contain only pertinent information on ASD. Second, we show this same model can
be used to learn a disentangled representation of multimodal biomarkers that
results in an increase in predictive performance. These results demonstrate
that adversarial methods can address both biomarker confounds and improve
biomarker predictive performance.
