In the present work, we address (i) by extending ACV to CV schemes with dependence structure between the folds.

Neural Information Processing Systems http://nips.cc/

Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the efficiency of learning on a novel task.

Neural Information Processing Systems http://nips.cc/

Now, since as mentioned above the KL divergence is a non-negative quantity, from Eq. S2 we have

Neural Networks can perform poorly when the training label distribution is heavily imbalanced, as well as when the testing data differs from the training distribution. In order to deal with shift in the testing label distribution, which imbalance causes, we motivate the problem from the perspective of an optimal Bayes classifier and derive a post-training prior rebalancing technique that can be solved through a KL-divergence based optimization. This method allows a flexible post-training hyper-parameter to be efficiently tuned on a validation set and effectively modify the classifier margin to deal with this imbalance. We further combine this method with existing likelihood shift methods, re-interpreting them from the same Bayesian perspective, and demonstrating that our method can deal with both problems in a unified way.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper presents a simple novel model for structured sparsity in spike-and-slab models and an expectation propagation algorithm for Bayesian inference. It is written clearly and accompanied by interesting examples and comparisons with other approaches. Q2: Please summarize your review in 1-2 sentences Although there are some previous works in this area, this particular approach is simple and novel. First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance.