  Bayesian optimization has recently attracted the attention of the automatic
machine learning community for its excellent results in hyperparameter tuning.
BO is characterized by the sample efficiency with which it can optimize
expensive black-box functions. The efficiency is achieved in a similar fashion
to the learning to learn methods: surrogate models (typically in the form of
Gaussian processes) learn the target function and perform intelligent sampling.
This surrogate model can be applied even in the presence of noise; however, as
with most regression methods, it is very sensitive to outlier data. This can
result in erroneous predictions and, in the case of BO, biased and inefficient
exploration. In this work, we present a GP model that is robust to outliers
which uses a Student-t likelihood to segregate outliers and robustly conduct
Bayesian optimization. We present numerical results evaluating the proposed
method in both artificial functions and real problems.
