This paper contains uncensored toxic content that might be offensive. Toxicity classification in textual content remains a significant problem.

Cross-V alidation (CV) is the default choice for estimate the out-of-sample performance of machine learning models.

We propose adaptive, line search-free second-order methods with optimal rate of convergence for solving convex-concave min-max problems.

However, the nonlinearity of risk measures makes it challenging to achieve convergence and optimality.