  Despite the success of automated machine learning (AutoML), which aims to
find the best design, including the architecture of deep networks and
hyper-parameters, conventional AutoML methods are computationally expensive and
hardly provide insights into the relations of different model design choices.
To tackle the challenges, we propose FALCON, an efficient sample-based method
to search for the optimal model design. Our key insight is to model the design
space of possible model designs as a design graph, where the nodes represent
design choices, and the edges denote design similarities. FALCON features 1) a
task-agnostic module, which performs message passing on the design graph via a
Graph Neural Network (GNN), and 2) a task-specific module, which conducts label
propagation of the known model performance information on the design graph.
Both modules are combined to predict the design performances in the design
space, navigating the search direction. We conduct extensive experiments on 27
node and graph classification tasks from various application domains, and an
image classification task on the CIFAR-10 dataset. We empirically show that
FALCON can efficiently obtain the well-performing designs for each task using
only 30 explored nodes. Specifically, FALCON has a comparable time cost with
the one-shot approaches while achieving an average improvement of 3.3% compared
with the best baselines.
