We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
— Computing the partition function and the marginals of a global probability distribution are two important issues in any probabilistic inference problem. In a previous work, we ...
We introduce a nonparametric representation for graphical model on trees which expresses marginals as Hilbert space embeddings and conditionals as embedding operators. This formul...
⎯ Technology scaling is in the era where the chip performance is constrained by its power dissipation. Although the power limits vary with the application domain, they dictate th...
We introduce a new algorithm for binary classification in the selective sampling protocol. Our algorithm uses Regularized Least Squares (RLS) as base classifier, and for this reas...