Somewhat more limited than your question, but I know two such papers: "Tunability: Importance of Hyperparameters of Machine Learning Algorithms" P. Probst et al., and "Hyperparameters and tuning strategies for random forest" P. Probst et al.

Both are on Arxiv. First one concerns tunability of multiple ML algorithms, i.e. how sensitive are they in general to hyperparameter choice. Second one delves deeper into the same area, but specifically for random forests, gathering results from many other works. Using those ideas, I was able to dramatically decrease the computational resources for tuning by better designing hyperparameter grids.