As a simple illustration of the benefits of multi-task GPs, we created example functions with one input and one output, then trained both a GP and multi-task GP on only three data points. In the multi-task GP case, we also generated 25 data points from an auxiliary task. As shown in Figure , when the main and the auxiliary tasks are similar, the predictions from the multi-task GP (shown as samples from the posterior of the GP) more accurately represent the underlying function than the predictions from the single-task GP. The multi-task GP leverages covariance between the data in the two tasks to improve predictions on the main task, even with limited data for the main task -this is shown formally in the Methods section. As shown in Figure , when the main and the auxiliary tasks are divergent, predictions from the single-task and multi-task GP are highly variable. However, this variability in the multi-task GP is still useful because the BO algorithm will explore to better capture the underlying distribution of the main task.