One of the most important steps toward model interpretability is feature selection, which aims to identify the subset of relevant features with respect to an outcome.

We investigate the robust PCA problem of decomposing an observed matrix into the sum of a low-rank and a sparse error matrices via convex programming Principal Component Pursuit (PCP). In contrast to previous studies that assume the support of the error matrix is generated by uniform Bernoulli sampling, we allow non-uniform sampling, i.e., entries of the low-rank matrix are corrupted by errors with unequal probabilities. We characterize conditions on error corruption of each individual entry based on the local incoherence of the low-rank matrix, under which correct matrix decomposition by PCP is guaranteed. Such a refined analysis of robust PCA captures how robust each entry of the low rank matrix combats error corruption. In order to deal with non-uniform error corruption, our technical proof introduces a new weighted norm and develops/exploits the concentration properties that such a norm satisfies.

Those scores are then averaged over the whole corpus to reach an overall quality. The MSCOCO dataset is one of the most representative large-scale labeled image datasets available to the public. It is also the most authoritative and important benchmark in the current target recognition, detection and other fields. Its image data source is Y ahoo's photo album website, Flickr. Most of the images in the dataset display a human being involved in an activity.

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

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

The community studying fairness in machine learning has yet to settle on definitions.

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

In above examples, each observed interaction normally indicates a user's interest on an item, i.e., a positive label, while the rest unobserved interactions