— In this paper, we perform a complete asymptotic performance analysis of the stochastic approximation algorithm (denoted subspace network learning algorithm) derived from Oja’...
Companies are developing a growing interest in aligning their information systems in a process-oriented way. However, current processaware information systems (PAIS) fail to meet p...
Stefanie Rinderle, Barbara Weber, Manfred Reichert...
We investigate the problem of learning a widely-used latent-variable model – the Latent Dirichlet Allocation (LDA) or “topic” model – using distributed computation, where ...
David Newman, Arthur Asuncion, Padhraic Smyth, Max...
We address classification problems for which the training instances are governed by a distribution that is allowed to differ arbitrarily from the test distribution--problems also ...
In supervised learning, we commonly assume that training and test data are sampled from the same distribution. However, this assumption can be violated in practice and then standa...