We present an alternative approach to decompose non-negative tensors, called many-body approximation .

We propose a new algorithm for the problem of recovering data that adheres to multiple, heterogeneous low-dimensional structures from linear observations.

Deep reinforcement learning (RL) has achieved remarkable successes in complex tasks, e.g Go [

To validate the effectiveness of SHOT, we conduct empirical tests on standard few-shot learning tasks and qualitatively analyze its dynamics.

In this work, we study the low-rank MDPs with adversarially changed losses in the full-information feedback setting.