  Hyperparameter optimization (HPO) is a fundamental problem in automatic
machine learning (AutoML). However, due to the expensive evaluation cost of
models (e.g., training deep learning models or training models on large
datasets), vanilla Bayesian optimization (BO) is typically computationally
infeasible. To alleviate this issue, Hyperband (HB) utilizes the early stopping
mechanism to speed up configuration evaluations by terminating those
badly-performing configurations in advance. This leads to two kinds of quality
measurements: (1) many low-fidelity measurements for configurations that get
early-stopped, and (2) few high-fidelity measurements for configurations that
are evaluated without being early stopped. The state-of-the-art HB-style
method, BOHB, aims to combine the benefits of both BO and HB. Instead of
sampling configurations randomly in HB, BOHB samples configurations based on a
BO surrogate model, which is constructed with the high-fidelity measurements
only. However, the scarcity of high-fidelity measurements greatly hampers the
efficiency of BO to guide the configuration search. In this paper, we present
MFES-HB, an efficient Hyperband method that is capable of utilizing both the
high-fidelity and low-fidelity measurements to accelerate the convergence of
HPO tasks. Designing MFES-HB is not trivial as the low-fidelity measurements
can be biased yet informative to guide the configuration search. Thus we
propose to build a Multi- Fidelity Ensemble Surrogate (MFES) based on the
generalized Product of Experts framework, which can integrate useful
information from multi-fidelity measurements effectively. The empirical studies
on the real-world AutoML tasks demonstrate that MFES-HB can achieve 3.3-8.9x
speedups over the state-of-the-art approach - BOHB.
