The k-Nearest Neighbors algorithm can be easily adapted to classify complex objects (e.g. sets, graphs) as long as a proper dissimilarity function is given over an input space. Bo...
Adam Woznica, Alexandros Kalousis, Melanie Hilario
Many applications of supervised learning require good generalization from limited labeled data. In the Bayesian setting, we can try to achieve this goal by using an informative pr...
Accommodating learning styles in adaptive educational systems represents an important step towards providing individualized instruction. The paper summarizes the main results repo...
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...
In this work we consider the problem of universal prediction of individual sequences where the universal predictor is a deterministic finite state machine, with a fixed, relativel...