Title: Meta-Active Learning in Probabilistically-Safe Optimization. Abstract: Learning to control a safety-critical system with latent dynamics (e.g.  for deep brain stimulation) requires judiciously taking calculated risks to gain information. We present a probabilistically-safe, meta-active learning approach to efficiently learn system dynamics and optimal configurations. The key to our approach is a novel integration of meta-learning and chance-constrained optimization in which we 1) meta-learn an LSTM-based embedding of the active learning sample history, 2) encode a deep learning-based acquisition function with this embedding into a mixed-integer linear program (MILP), and 3) solve the MILP to find the optimal action trajectory, trading off the predicted information gain from the acquisition function and the likelihood of safe control. We set a new state-of-the-art in active learning to control a high-dimensional system with latent dynamics, achieving a 46% increase in information gain and a 20% speedup in computation time.  We then outperform baseline methods in learning the optimal parameter settings for deep brain stimulation in rats to enhance the rats’ performance on a cognitive task while safely avoiding unwanted side effects (i.e., triggering seizures).