Workshop IV: Efficient Tensor Representations for Learning and Computational Complexity

The first emphasis of this workshop will be on the theory of recovering efficient tensor representations from empirical data, as studied e.g. in the context of low-rank tensor completion or matrix-product state learning. We will focus both on algorithmic and on statistical aspects. The second emphasis of the workshop will be on applications of efficient tensor representations to theoretical computer science, particularly computational complexity theory. A closely related area that will also be covered is the resource theory of tensors in quantum information theory.