most of these results have not used distributional assumptions in any meaningful way I would say I mean there's a lot of results about learning single relu single convolutional filters they use Gaussian assumptions of course and those argue and even then you can see the Gaussian are helping you that much there were other ways to learn it just maybe not quite gradient descent so what are some reasonable distributional assumptions that can help us learn things via kernels don't really depend on the distribution their optimization