Manifold estimation and singular deconvolution under Hausdorff loss

Manifold learning is an area of intense research activity in machine learning and statistics. Yet a very basic question about manifold learning is still open, namely, how well can we estimate a manifold from n noisy samples? In this paper we investigate this question under various assumptions.