  An essential problem in automated machine learning (AutoML) is that of model
selection. A unique challenge in the sequential setting is the fact that the
optimal model itself may vary over time, depending on the distribution of
features and labels available up to each point in time. In this paper, we
propose a novel Bayesian optimization (BO) algorithm to tackle the challenge of
model selection in this setting. This is accomplished by treating the
performance at each time step as its own black-box function. In order to solve
the resulting multiple black-box function optimization problem jointly and
efficiently, we exploit potential correlations among black-box functions using
deep kernel learning (DKL). To the best of our knowledge, we are the first to
formulate the problem of stepwise model selection (SMS) for sequence
prediction, and to design and demonstrate an efficient joint-learning algorithm
for this purpose. Using multiple real-world datasets, we verify that our
proposed method outperforms both standard BO and multi-objective BO algorithms
on a variety of sequence prediction tasks.
