We propose to learn an active learning policy for inverse problem optimisation.
We propose to learn a switching policy for inverse problem optimisation.
We propose a novel learning-active-learning method that can adapt to different task objectives.
We propose an E-values based classifier two-sample test that has much stronger finite-sample type-I error control than existing methods.