On the paper, I was hoping you could help me understand a few things: - It seems that the main finding is that any k-CNF form, like Thomas' Boolean Regulatory Network, can be expressed by PAC learning bounds. In section 4 of the abstract [1], you mentioned that "when the dimension increases... the PAC learning algorithm can leverage available prior knowledge...". Are you referring to the time dimension adding more clauses to the k-CNF? - I'm having trouble reconciling the PAC term "h" with "model confidence" in section 5.2. Is this allowed because the PAC learning "delta" (probability) [2] parameter is dropped for the k-CNF adaptation? - In this concrete case, is the learning portion just the mapping the stochastic traces to outputs (i.e. lookups)? I'm missing some understanding on how such a mapping handles stochasticity.
You'll have to forgive me, as I'm still trying to understand the paper. It's incredibly interesting to me, so thanks for writing it!
[1] Abstract - http://mlsb.cc/2017/abstracts/MLSB_2017_paper_11.pdf
[2] PAC Learning - https://en.wikipedia.org/wiki/Probably_approximately_correct...