Skip to yearly menu bar Skip to main content


Learning Globally Smooth Functions on Manifolds

Juan Cervino · Luiz Chamon · Benjamin Haeffele · Rene Vidal · Alejandro Ribeiro

Exhibit Hall 1 #134
[ ]
[ PDF [ Poster


Smoothness and low dimensional structures play central roles in improving generalization and stability in learning and statistics. This work combines techniques from semi-infinite constrained learning and manifold regularization to learn representations that are globally smooth on a manifold. To do so, it shows that under typical conditions the problem of learning a Lipschitz continuous function on a manifold is equivalent to a dynamically weighted manifold regularization problem. This observation leads to a practical algorithm based on a weighted Laplacian penalty whose weights are adapted using stochastic gradient techniques. It is shown that under mild conditions, this method estimates the Lipschitz constant of the solution, learning a globally smooth solution as a byproduct. Experiments on real world data illustrate the advantages of the proposed method relative to existing alternatives. Our code is available at

Chat is not available.