  Langevin dynamics (LD) is widely used for sampling from distributions and for
optimization. In this work, we derive a closed-form expression for the expected
loss of preconditioned LD near stationary points of the objective function. We
use the fact that at the vicinity of such points, LD reduces to an
Ornstein-Uhlenbeck process, which is amenable to convenient mathematical
treatment. Our analysis reveals that when the preconditioning matrix satisfies
a particular relation with respect to the noise covariance, LD's expected loss
becomes proportional to the rank of the objective's Hessian. We illustrate the
applicability of this result in the context of neural networks, where the
Hessian rank has been shown to capture the complexity of the predictor function
but is usually computationally hard to probe. Finally, we use our analysis to
compare SGD-like and Adam-like preconditioners and identify the regimes under
which each of them leads to a lower expected loss.
