We consider outlier-robust and sparse estimation of linear regression coefficients, when the covariates and the noises are contaminated by adversarial outliers and noises are sampled from a heavy-tailed distribution. Our results present sharper error bounds under weaker assumptions than prior studies that share similar interests with this study. Our analysis relies on some sharp concentration inequalities resulting from generic chaining.

翻译：我们考虑在协变量和噪声均受到对抗性离群污染且噪声来自重尾分布的情形下，对线性回归系数进行离群鲁棒与稀疏估计。与先前类似研究相比，我们的结果在更弱的假设下给出了更紧的误差界。我们的分析依赖于由通用链导出的若干精确集中不等式。