title: Robust and Scalable SDE Learning: A Functional Perspective
abstract: Stochastic differential equations provide a rich class of flexible generative
  models, capable of describing a wide range of spatio-temporal processes. A host
  of recent work looks to learn data-representing SDEs, using neural networks and
  other flexible function approximators. Despite these advances, learning remains
  computationally expensive due to the sequential nature of SDE integrators. In
  this work, we propose an importance-sampling estimator for probabilities of
  observations of SDEs for the purposes of learning. Crucially, the approach we
  suggest does not rely on such integrators. The proposed method produces
  lower-variance gradient estimates compared to algorithms based on SDE
  integrators and has the added advantage of being embarrassingly parallelizable.
  This facilitates the effective use of large-scale parallel hardware for massive
  decreases in computation time.
  
authors: Scott Alexander Cameron Tyron Luke Cameron Arnu Pretorius Stephen J. Roberts
hIndex: Stephen J. Roberts 12