Paper

We introduce PRISM, a framework that disentangles architectural priors for dataset distillation, outperforming single-teacher setups.

This paper proposes a new method to parameterize open loop controls in stochastic optimal control problems using path signatures. We show that these controls are dense in the space of all admissible controls and establish conditions for stability of the controlled dynamics and target functional.