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Nonparametric active learning for cost-sensitive classification
Cost-sensitive learning is a common type of machine learning problem where different errors of prediction incur different costs.
Nonparametric adaptive active learning under local smoothness condition
Active learning is typically used to label data, when the labeling process is expensive.
Deep matrix factorizations
Constrained low-rank matrix approximations have been known for decades as powerful linear dimensionality reduction techniques to be able to extract the information contained in large data sets in a relevant way.
K-NN active learning under local smoothness assumption
Additionally, our algorithm avoids the strong density assumption that supposes the existence of the density function of the marginal distribution of the instance space and is therefore more generally applicable.
Near-Convex Archetypal Analysis
Archetypal analysis (AA), also referred to as convex NMF, is a well-known NMF variant imposing that the basis elements are themselves convex combinations of the data points.
K-nn active learning under local smoothness condition
There is a large body of work on convergence rates either in passive or active learning.
