  We establish a Berry--Esseen bound for general multivariate nonlinear
statistics by developing a new multivariate-type randomized concentration
inequality. The bound is the best possible for many known statistics. As
applications, Berry--Esseen bounds for M-estimators and averaged stochastic
gradient descent algorithms are obtained.
