Now showing items 13-21 of 21

    • Particle Gibbs for Infinite Hidden Markov Models 

      Tripuranen, Nilesh; Gu, Shixiang; Ge, Hong; Ghahramani, Zoubin (Curran Associates, 2015-12-18)
      Infinite Hidden Markov Models (iHMM’s) are an attractive, nonparametric generalization of the classical Hidden Markov Model which can automatically infer the number of hidden states in the system. However, due to the ...
    • Practical Probabilistic Programming with Monads 

      Scibior, Adam; Ghahramani, Zoubin; Gordon, Andrew D. (ACM, 2015-07-30)
      The machine learning community has recently shown a lot of interest in practical probabilistic programming systems that target the problem of Bayesian inference. Such systems come in different forms, but they all express ...
    • Predictive Entropy Search for Bayesian Optimization with Unknown Constraints 

      Hernández-Lobato, José Miguel; Gelbart, Michael A.; Hoffman, Matthew W.; Adams, Ryan P.; Ghahramani, Zoubin (JMLR, 2015-06-01)
      Unknown constraints arise in many types of expensive black-box optimization problems. Several methods have been proposed recently for performing Bayesian optimization with constraints, based on the expected improvement ...
    • Probabilistic machine learning and artificial intelligence 

      Ghahramani, Zoubin (NPG, 2015-05-27)
      How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing ...
    • R/BHC: fast Bayesian hierarchical clustering for microarray data 

      Savage, Richard S.; Heller, Katherine; Xu, Yang; Ghahramani, Zoubin; Truman, William M.; Grant, Murray; Denby, Katherine J. et al. (2009-08-06)
      Abstract Background Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data analysis, little attention has been paid to ...
    • Scalable Discrete Sampling as a Multi-Armed Bandit Problem 

      Chen, Yutian; Ghahramani, Zoubin (2016)
      Drawing a sample from a discrete distribution is one of the building components for Monte Carlo methods. Like other sampling algorithms, discrete sampling suffers from the high computational burden in large-scale ...
    • Scalable Variational Gaussian Process Classification 

      Hensman, James; Matthews, Alexander G. de G.; Ghahramani, Zoubin (JMLR, 2015-02-21)
      Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark ...
    • Towards exploratory faceted search systems 

      Ksikes, Alex (2014-03-04)
      In this thesis, we cover what we believe would be the main ingredients of an exploratory search system (ESS). In a nutshell, these are textual queries, facets, visual results, social search and query-by-example. The goal ...
    • A unified framework for resource-bounded autonomous agents interacting with unknown environments 

      Ortega, Pedro Alejandro Jr (2011-07-12)
      The aim of this thesis is to present a mathematical framework for conceptualizing and constructing adaptive autonomous systems under resource constraints. The first part of this thesis contains a concise presentation of ...