Reviews: Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model

The primary originality of this paper derives from dealing with active-learning regime with little or no data. This is an extremely important problem for ML, especially as ML is applied to more real-world domains where data is minimal and collection is expensive. The significance of this problem is therefore of high significance. I will discuss the significance their approach to the problem below. Related to this first point, the authors do a fantastic job of situating themselves in the wider active-learning literature, highlighting where there "ice-problem" sits and specifying its unique differences to alternative active learning scenarios.