Setting regularization parameters for variational estimators in imaging or machine learning is notoriously difficult. Grid-search requires to choose a predefined grid of parameters and scales exponentially in the number of parameters which can be quickly inconvenient or even impossible in imaging. Another class of approaches casts hyperparameter optimization as a bi-level optimization problem, typically solved by gradient descent. The key challenge for these approaches is the estimation of the gradient w.r.t. the hyperparameters. In this presentation, I will show algorithmic/automatic differentiation can help to overcome this challenge, both for inverse problems with a differentiable Stein Unbiased Risk Estimator and in regression using held-out loss.