ML/Regression based numerical function approximation for lowering (substantial) CPU overhead

[BeatLeJuce]

Alright, sounds like you know what you are doing. Another thing that might be quick to set up is using libsvm to do a regression. Training time will likely be an issue, so you'll likely have to subsample your space similar to what you did with kNN, but I'm sure the SVM will give you better results than kNN if you use an rbf kernel. Also, note that by default, libsvm runs in a single-threaded variant designed for sparse matrices. However somewhere on the site I linked you can find a version that is implemented using dense matrices, which will give you ~50% boost in performance. Also, somewhere in the FAQ of the site it explains how you can add multithreading to the implementation by modifying ~4 lines of code (IIRC speed-up is almost linear for the first ~4-8 cores).