Interface

• Document what generate_blabla does
  • generate_maximizer does not return a differentiable layer
  • in particular for generate_maximizer the signature (args and kwargs) of the returned closure
• How to include losses in addition to CO layers?
  • Callable struct that combines model, CO layer and loss? Not ideal, better leave ingredients separate
• Add function for turnkey training?
  • Or a struct that stores the whole dataset

Getting data

• DataDeps.jl
• DataToolkit.jl

Data sources

• Energy-cost aware scheduling + knapsack:
  • https://link.springer.com/chapter/10.1007/978-3-642-33558-7_68
  • https://github.com/JayMan91/aaai_predit_then_optimize (easier)
  • https://www.csplib.org/Problems/prob059/ (different?)

Problem meaning

• Subset selections:
  • artificial split: top $k$ becomes 1) linear model to get cost followed by 2) LP
  • the optimal statistical model is identity (but the computer doesn't know)

Varying instance sizes

• Modify ShortestPathBenchmark to draw a random grid size from specified ranges of height and width, then see what you need in the interface to make it work