Review for NeurIPS paper: Assisted Learning: A Framework for Multi-Organization Learning

Weaknesses: The paper states that model selection or model averaging approaches will not significantly improve over the best of the models (Alice's or Bob's) used in the assisted learning procedure because they fail to utilize the full data (the union of Alice's and Bob's features). However, ensemble techniques such as stacked regression (Breiman 1996) are often successfully used to improve predictive performance by combining not only different models trained on the same set of features, but also by combining different models trained on different subsets of features. In all experiments performed in the paper, only comparisons between assisted learning and the oracle model were presented. The paper would be considerably stronger if it was able to show that assisted learning compared favorably against (for instance) a stacked model generated with the predictions obtained from the different models on modules M_1, …, M_m (trained with the original public responses). Note that under the assumptions made by the paper, that the labels/response (as well as, some sort of identifier needed to collate the labels/response to the features) are public available, a simpler ensemble approach (such as stacking) could also be directly used to improve learning without sharing the private feature data.