I'm particularly proud of this meta approach and I am actually thinking this could become huge: the same thing can be done for hyperparameter optimization in machine learning tasks.
There is already a substantial field of Machine Learning/Meta Learning which focuses on exactly this. For example, this paper [1] from NeurIPS 2015 does exactly what you suggest.
Yea I am aware of meta hyperparameter approach for ML, except they only focus on accuracy instead of also including train/prediction times in to the equation :) That's what I was referring to! (you can save A LOT of compute and zoom in on things that work if you can weed out slow / badly performing algorithms as part of meta learning hyperparameters).
To make it extra clear: by doing a lot of compute on different datasets and not only recording the accuracy but also time it took, and then by including that as dimension it will even give better results.
Comments
There is already a substantial field of Machine Learning/Meta Learning which focuses on exactly this. For example, this paper [1] from NeurIPS 2015 does exactly what you suggest.
[1]: https://papers.nips.cc/paper/5872-efficient-and-robust-autom...
Yea I am aware of meta hyperparameter approach for ML, except they only focus on accuracy instead of also including train/prediction times in to the equation :) That's what I was referring to! (you can save A LOT of compute and zoom in on things that work if you can weed out slow / badly performing algorithms as part of meta learning hyperparameters).
To make it extra clear: by doing a lot of compute on different datasets and not only recording the accuracy but also time it took, and then by including that as dimension it will even give better results.