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19962020
most citedDropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

2.6k citations · 11.1k across the 96 of their papers we have counts for

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Showing 2014 · stat.MLShow all

12 papers · 2 filters

stat.ML2014★ 346 cited

Scalable Variational Gaussian Process Classification

James Hensman, Alex Matthews, Zoubin Ghahramani

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, outperformi…

stat.ML2014★ 3 cited

Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs

Yutian Chen, Vikash Mansinghka, Zoubin Ghahramani

Probabilistic programming languages can simplify the development of machine learning techniques, but only if inference is sufficiently scalable. Unfortunately, Bayesian parameter e…

stat.ML2014★ 2 cited

Beta diffusion trees and hierarchical feature allocations

Creighton Heaukulani, David A. Knowles, Zoubin Ghahramani

We define the beta diffusion tree, a random tree structure with a set of leaves that defines a collection of overlapping subsets of objects, known as a feature allocation. A genera…

stat.ML2014★ 1 cited

Classification using log Gaussian Cox processes

Alexander G. de. G Matthews, Zoubin Ghahramani

McCullagh and Yang (2006) suggest a family of classification algorithms based on Cox processes. We further investigate the log Gaussian variant which has a number of appealing prop…

stat.ML2014★ 398 cited

Predictive Entropy Search for Efficient Global Optimization of Black-box Functions

José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that ma…

stat.ML2014★ 380 cited

Linear Dimensionality Reduction: Survey, Insights, and Generalizations

John P. Cunningham, Zoubin Ghahramani

Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to their simple geometric interpretations and typically attractive computational p…