Saksham Kiroriwal

Bayesian Optimization with Fisher Information Geometry: Gradient Bounds and Trust-Region Methods

We study Bayesian optimization (BO) through the lens of information geometry. Pulling back the Fisher information metric through the surrogate posterior map yields a local sensitivity tensor on the input space, which leads to an upper bound on the gradient of reparameterizable acquisition functions. This view explains...