  Many AutoML problems involve optimizing discrete objects under a black-box
reward. Neural-guided search provides a flexible means of searching these
combinatorial spaces using an autoregressive recurrent neural network. A major
benefit of this approach is that builds up objects sequentially--this provides
an opportunity to incorporate domain knowledge into the search by directly
modifying the logits emitted during sampling. In this work, we formalize a
framework for incorporating such in situ priors and constraints into
neural-guided search, and provide sufficient conditions for enforcing
constraints. We integrate several priors and constraints from existing works
into this framework, propose several new ones, and demonstrate their efficacy
in informing the task of symbolic regression.
