An unrestricted learning procedure

We study learning problems involving arbitrary classes of functions F, underlying measures μ, and targets Y. Because proper learning procedures, i.e., procedures that are only allowed to select functions in F, tend to perform poorly unless the problem satisfies some additional structural property (e.g., that F is convex), we consider unrestricted learning procedures that are free to choose functions outside the given class. We present a new unrestricted procedure whose sample complexity is almost the best that one can hope for and holds for (almost) any problem, including heavy-tailed situations. Moreover, the sample complexity coincides with what one could expect if F were convex, even when F is not. And if F is convex, then the unrestricted procedure turns out to be proper.