3 accepted papers
We study the problem of rank aggregation with features, where both pairwise comparisons and item features are available to help the rank aggregation task. Observing that traditional rank aggregation methods disregard features, while models adapted from learning-to-rank task are sensitive to feature…
The robust principal component analysis (robust PCA) problem has been considered in many machine learning applications, where the goal is to decompose the data matrix as a low rank part plus a sparse residual. While current approaches are developed by only considering the low rank plus sparse struct…
We study matrix completion problem with side information. Side information has been considered in several matrix completion applications, and is generally shown to be useful empirically. Recently, Xu et al. studied the effect of side information for matrix completion under a theoretical viewpoint,…