2.6k citations · 11.1k across the 96 of their papers we have counts for
15 papers · 2 filters
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Yarin Gal, Zoubin Ghahramani
Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Yet a major difficulty with these models is their tendency to overfit, with dr…
Sandwiching the marginal likelihood using bidirectional Monte Carlo
Roger B. Grosse, Zoubin Ghahramani, Ryan P. Adams
Computing the marginal likelihood (ML) of a model requires marginalizing out all of the parameters and latent variables, a difficult high-dimensional summation or integration probl…
A General Framework for Constrained Bayesian Optimization using Information-based Search
José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams +2
We present an information-theoretic framework for solving global black-box optimization problems that also have black-box constraints. Of particular interest to us is to efficientl…
Dirichlet Fragmentation Processes
Hong Ge, Yarin Gal, Zoubin Ghahramani
Tree structures are ubiquitous in data across many domains, and many datasets are naturally modelled by unobserved tree structures. In this paper, first we review the theory of ran…
An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process
Amar Shah, David A. Knowles, Zoubin Ghahramani
Stochastic variational inference (SVI) is emerging as the most promising candidate for scaling inference in Bayesian probabilistic models to large datasets. However, the performanc…
MCMC for Variationally Sparse Gaussian Processes
James Hensman, Alexander G. de G. Matthews, Maurizio Filippone +1
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 compu…