In this paper, we propose an efficient algorithm to accelerate computing the TT decomposition with the Alternating Least Squares (ALS) algorithm relying on exact leverage scores sampling.

We revisit this scenario as an experimental case study in Section 6.

Designing reward functions for efficiently guiding reinforcement learning (RL) agents toward specific behaviors is a complex task.

We theoretically investigate offline multitask low-rank RL, and propose a new algorithm called MORL for offline multitask representation learning.