Video Lectures

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Several equivalent definitions of rank for matrices yield non-equivalent definitions of rank when generalized to higher order tensors. Understanding the interplay between these different definitions is related to important questions in additive...

Suppose we are given a random rank-r order-3 tensor---that is, an n-by-n-by-n array of numbers that is the sum of r random rank-1 terms---and our goal is to recover the individual rank-1 terms. In principle this decomposition task is possible when r<

In recent years, the average-case complexity of various high-dimensional statistical tasks has been resolved in restricted-but-powerful models of computation such as statistical queries, sum-of-squares, or low-degree polynomials. However, the hardness of tensor decomposition has remained elusive, and I will explain what makes this problem difficult. We show the first formal hardness for average-case tensor decomposition: when r>>n3/2, the decomposition task is hard for algorithms that can be expressed as low-degree polynomials in the tensor entries.

What is the symplectic analogue of being convex? We shall present different ideas to approach this question. Along the way, we shall present recent joint results with J.Dardennes and J.Zhang on monotone toric domains non-symplectomorphic to convex...