When it comes to getting stuck at a local optimum, I think it’s the convexity of the loss function that matters, not just the convexity of the parameter space.

When it comes to getting stuck at a local optimum, I think it’s the convexity of the loss function that matters, not just the convexity of the parameter space. As I understand it, this result says that for convex losses, some simpler samplers work near enough to ideally.