  We propose a new method for automatically detecting monotonic input-output
relationships from data using Gaussian Process (GP) models with virtual
derivative observations. Our results on synthetic and real datasets show that
the proposed method detects monotonic directions from input spaces with high
accuracy. We expect the method to be useful especially for improving
explainability of the models and improving the accuracy of regression and
classification tasks, especially near the edges of the data or when
extrapolating.
