Browsing by Author "Ghahramani, Zoubin"
Now showing items 6-16 of 16
-
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
(Microtome Publishing, 2015)Multivariate categorical data occur in many applications of machine learning. One of the main difficulties with these vectors of categorical variables is sparsity. The number of possible observations grows exponentially ... -
Linear Dimensionality Reduction: Survey, Insights, and Generalizations
(MIT Press, 2015-12-01)Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to their simple geometric interpretations and typically attractive computational properties. These methods capture many data ... -
MCMC for Variationally Sparse Gaussian Processes
(Neural Information Processing Systems Foundation, 2015-12-07)Gaussian process (GP) models form a core part of probabilistic machine learning. Considerable research effort has been made into attacking three issues with GP models: how to compute efficiently when the number of data is ... -
Neural Adaptive Sequential Monte Carlo
(Curran Associates, 2015)Sequential Monte Carlo (SMC), or particle filtering, is a popular class of methods for sampling from an intractable target distribution using a sequence of simpler intermediate distributions. Like other importance ... -
Particle Gibbs for Infinite Hidden Markov Models
(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
(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
(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
(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
(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
(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
(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 ...
