We introduce a new gradient noise model for stochastic convex optimization and apply it to achieve state-of-the-art rates in both quantum and non-quantum settings.

We give a nearly-linear time algorithm for privately computing the geometric median, addressing the main open question of Haghifam et al (NeurIPS '24).