  Given a publicly available pool of machine learning models constructed for
various tasks, when a user plans to build a model for her own machine learning
application, is it possible to build upon models in the pool such that the
previous efforts on these existing models can be reused rather than starting
from scratch? Here, a grand challenge is how to find models that are helpful
for the current application, without accessing the raw training data for the
models in the pool. In this paper, we present a two-phase framework. In the
upload phase, when a model is uploading into the pool, we construct a reduced
kernel mean embedding (RKME) as a specification for the model. Then in the
deployment phase, the relatedness of the current task and pre-trained models
will be measured based on the value of the RKME specification. Theoretical
results and extensive experiments validate the effectiveness of our approach.
