Review for NeurIPS paper: Learning Differentiable Programs with Admissible Neural Heuristics

Generating programs has been a long-standing problem in AI for many decades. Reviewers found valuable the fact that this approach combines prior literature on heuristic search with a modern neural networks approach to improve performance. Reviewers also found that methods which combine discrete and continuous parts of programs are in short supply, making this of wide interest and likely to spur further research. The fact that the approach is in a sense straightforward conceptually but not obvious while being able to perform when more complex methods like TerpreT are not suitable was also pointed out as a significant advance. Reviewers wished to see more related to the interpretability of the acquired models.