> as purely empirical as the gradient descent loops
Are you suggesting that gradient descent is an empirically found and not understood technique? It was originally proposed by Cauchy in 1847, its properties are very well understood.
You might be referring to properties of the domains its being applied to.
I’m not saying gradient descent was empirically discovered, I’m saying that its use in machine learning (or elsewhere, I suppose) is itself a form of empiricism in that what it is is essentially a repeated observe/measure error/adjust cycle.
> I’m not saying gradient descent was empirically discovered, I’m saying that its use in machine learning is itself a form of empiricism. A repeated observe/adjust-based-on-data cycle
The data is the input, the output is to generally find the lowest amount of a loss function. It’s a greedy approach because brute forcing is inefficient.
It’s no more empirical than a greedy algorithm for scheduling.
> It’s no more empirical than a greedy algorithm for scheduling.
Right, GP is drawing a distinction between search, ie mechanical exploration of a space, with understanding, ie having a map of the territory such that you don’t need trial and error.
> its use in machine learning (or elsewhere, I suppose) is itself a form of empiricism in that what it is is essentially a repeated observe/measure error/adjust cycle.
"Empiricism" implies that the technique is based on observable, but not mathematically proven foundations. If a problem space is convex, gradient descent is guaranteed to converge to a global optimal solution, regardless of whether you know the exact formulation of the space.
Applying it when you don't understand if a space is convex is another question, but that's not a fault of gradient descent.