35 citations · 115 across the 10 of their papers we have counts for
12 papers
On Transferring Transferability: Towards a Theory for Size Generalization
Eitan Levin, Yuxin Ma, Mateo Díaz +1
Many modern learning tasks require models that can take inputs of varying sizes. Consequently, dimension-independent architectures have been proposed for domains where the inputs a…
Dimension-Free Descriptions of Convex Sets
Eitan Levin, Venkat Chandrasekaran
Convex sets arising in a variety of applications are well-defined for every relevant dimension. Examples include the simplex and the spectraplex that correspond to probability dist…
Any-dimensional equivariant neural networks
Eitan Levin, Mateo Díaz
Traditional supervised learning aims to learn an unknown mapping by fitting a function to a set of input-output pairs with a fixed dimension. The fitted function is then defined on…
The effect of smooth parametrizations on nonconvex optimization landscapes
Eitan Levin, Joe Kileel, Nicolas Boumal
We develop new tools to study landscapes in nonconvex optimization. Given one optimization problem, we pair it with another by smoothly parametrizing the domain. This is either for…
Finding stationary points on bounded-rank matrices: A geometric hurdle and a smooth remedy
Eitan Levin, Joe Kileel, Nicolas Boumal
We consider the problem of provably finding a stationary point of a smooth function to be minimized on the variety of bounded-rank matrices. This turns out to be unexpectedly delic…
A note on Douglas-Rachford, gradients, and phase retrieval
Eitan Levin, Tamir Bendory
The properties of gradient techniques for the phase retrieval problem have received a considerable attention in recent years. In almost all applications, however, the phase retriev…