  We present an improved method for symbolic regression that seeks to fit data
to formulas that are Pareto-optimal, in the sense of having the best accuracy
for a given complexity. It improves on the previous state-of-the-art by
typically being orders of magnitude more robust toward noise and bad data, and
also by discovering many formulas that stumped previous methods. We develop a
method for discovering generalized symmetries (arbitrary modularity in the
computational graph of a formula) from gradient properties of a neural network
fit. We use normalizing flows to generalize our symbolic regression method to
probability distributions from which we only have samples, and employ
statistical hypothesis testing to accelerate robust brute-force search.
