Tensor Cumulants for Statistical Inference on Invariant Tensor Distributions

The paper introduces a new set of objects called "tensor cumulants" that provide an explicit, near-orthogonal basis for invariant polynomials of tensors. These tensor cumulants generalize aspects of free probability theory for random matrices and enable new results for tensor PCA and distinguishing Wigner from Wishart tensors.