A large number of learning algorithms, for example, spectral clustering, kernel Principal Components Analysis and many manifold methods are based on estimating eigenvalues and eig...
A new algorithm for building decision tree classifiers is proposed. The algorithm is executed in a distributed environment and is especially designed for classifying large datase...
Sequential algorithms of active learning based on the estimation of the level sets of the empirical risk are discussed in the paper. Localized Rademacher complexities are used in ...
We present an approximate inference approach to parameter estimation in a spatio-temporal stochastic process of the reaction-diffusion type. The continuous space limit of an infer...
We study the problem of active learning with convex loss functions. We prove that even under bounded noise constraints, the minimax rates for proper active learning are often no b...