Reguralization = Robustness

I've recently come across a nice article which describes the relationsihp between reguralization and robustness from the machine learning perspective. Even though the article itself is and old blog post I still find valuable from my own work, and especially for someone who is new in the world of machine learning trying to understand and getter a better grasp of the field. From that artcile I kept out two things:

1. reguralization \(\rightarrow\) stable/stability \(\rightarrow\) robust
2. We cannot learn from noisy data without some form of reguralization, because we end up fitting the noise. I.e. slack variables in SVM, needed when data are not linearly separable. Not entirely true since slack variables are still needed even when data might be separable.