  The rising growth of deep neural networks (DNNs) and datasets in size
motivates the need for efficient solutions for simultaneous model selection and
training. Many methods for hyperparameter optimization (HPO) of iterative
learners, including DNNs, attempt to solve this problem by querying and
learning a response surface while searching for the optimum of that surface.
However, many of these methods make myopic queries, do not consider prior
knowledge about the response structure, and/or perform a biased cost-aware
search, all of which exacerbate identifying the best-performing model when a
total cost budget is specified. This paper proposes a novel approach referred
to as {\bf B}udget-{\bf A}ware {\bf P}lanning for {\bf I}terative Learners
(BAPI) to solve HPO problems under a constrained cost budget. BAPI is an
efficient non-myopic Bayesian optimization solution that accounts for the
budget and leverages the prior knowledge about the objective function and cost
function to select better configurations and to take more informed decisions
during the evaluation (training). Experiments on diverse HPO benchmarks for
iterative learners show that BAPI performs better than state-of-the-art
baselines in most cases.
