  Calibration is a highly challenging task, in particular in multiple yield
curve markets. This paper is a first attempt to study the chances and
challenges of the application of machine learning techniques for this. We
employ Gaussian process regression, a machine learning methodology having many
similarities with extended Kalman filtering - a technique which has been
applied many times to interest rate markets and term structure models.
  We find very good results for the single curve markets and many challenges
for the multi curve markets in a Vasicek framework. The Gaussian process
regression is implemented with the Adam optimizer and the non-linear conjugate
gradient method, where the latter performs best. We also point towards future
research.
