Sampling functions in Gaussian process (GP) models is challenging because of the highly correlated posterior distribution. We describe an efficient Markov chain Monte Carlo algori...
Michalis Titsias, Neil D. Lawrence, Magnus Rattray
We present a new class of models for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse line...
Pradeep D. Ravikumar, Han Liu, John D. Lafferty, L...
In this paper, we study the behavior of collaborative filtering based recommendations under evolving user profile scenarios. We propose a systematic validation methodology that ...
Olfa Nasraoui, Jeff Cerwinske, Carlos Rojas, Fabio...
Minimization methods for training feed-forward networks with Backpropagation are compared. Feedforward network training is a special case of functional minimization, where no expli...
Syed Muhammad Aqil Burney, Tahseen Ahmed Jilani, C...
We describe the application of kernel methods to Natural Language Processing (NLP) problems. In many NLP tasks the objects being modeled are strings, trees, graphs or other discre...