TenIPS: Inverse Propensity Sampling for Tensor Completion

Understand

Tensors are widely used to represent multiway arrays of data.

• The recovery of missing entries in a tensor has been extensively studied, generally under the assumption that entries are missing completely at random (MCAR).
• However, in most practical settings, observations are missing not at random (MNAR): the probability that a given entry is observed (also called the propensity) may depend on other entries in the tensor or even on the value of the missing entry.
• In this paper, we study the problem of completing a partially observed tensor with MNAR observations, without prior information about the propensities.