e96ed478dab8595a7dbda4cbcbee168f-Reviews.html

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes a simple latent factor model for one-shot learning with continuous outputs where very few observations are available. Specifically, it derives risk approximations in an asymptotic regime where the number of training examples is fixed and the number of features in the X space diverges. Based on principal component regression (PCR) estimator, two estimators including the bias-corrected estimator and the so-called oracle estimator are proposed and the bounds for the risks of these estimators are derived. These bounds provide insights into the significance of various parameters relevant to one-shot learning.