Review for NeurIPS paper: PyGlove: Symbolic Programming for Automated Machine Learning

Summary and Contributions: The paper introduces an AutoML library that tries to find its own sweet spot in the large ecosystem of newly minted AutoML libraries. The paper introduces a symbolic frontend to build neural network models, with simple fundamental constructs that provide choice insertions. Unlike all other packages that I have seen and reviewed, such as Keras Tuner, NNI, AutoGluon, Optuna (btw reference missing to Optuna, you should consider adding), this paper introduces something innovative and elegant. All these other packages consistently suffer from the code of the model definition getting ugly and unweildy really quickly when you have to introduce model structure searches, and when there's interaction between structure searches and size searches. In this paper, the authors cleanly separate model structure definitions from each layer's hyperparameter choices.