  We present a unified framework for computing CVA sensitivities, hedging the
CVA, and assessing CVA risk, using probabilistic machine learning meant as
refined regression tools on simulated data, validatable by low-cost companion
Monte Carlo procedures. Various notions of sensitivities are introduced and
benchmarked numerically. We identify the sensitivities representing the best
practical trade-offs in downstream tasks including CVA hedging and risk
assessment.
