This paper investigates robust low-rank tensor regression with only finite $(1+\epsilon)$-th moment noise based on the generalized tensor estimation framework proposed by Han et al. (2022). The theoretical result shows that when $\epsilon \geq 1$, the robust estimator possesses the minimax optimal rate. While $1> \epsilon>0$, the rate is slower than the deviation bound of sub-Gaussian tails.

翻译：本文根据Han等人(2022年)提出的通用的“压力估计框架”(2022年),对仅限值(1 ⁇ - ⁇ - ⁇ - ⁇ -美元)第1秒噪音的稳健低端重压回归进行了调查,理论结果表明,当$-epsilon\geq 1美元时,强度估算器拥有最小最大最佳比率。尽管1美元 > ⁇ 0美元,但该比率比亚百日元尾巴的偏差约束值要慢。