625 citations · 2.7k across the 69 of their papers we have counts for
5 papers · 2 filters
GLASSES: Relieving The Myopia Of Bayesian Optimisation
Javier González, Michael Osborne, Neil D. Lawrence
We present GLASSES: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search. The majority of global optimisation approaches in use are myopic, in…
Semi-described and semi-supervised learning with Gaussian processes
Andreas Damianou, Neil D. Lawrence
Propagating input uncertainty through non-linear Gaussian process (GP) mappings is intractable. This hinders the task of training GPs using uncertain and partially observed inputs.…
Spike and Slab Gaussian Process Latent Variable Models
Zhenwen Dai, James Hensman, Neil Lawrence
The Gaussian process latent variable model (GP-LVM) is a popular approach to non-linear probabilistic dimensionality reduction. One design choice for the model is the number of lat…
Bayesian Optimization for Synthetic Gene Design
Javier González, Joseph Longworth, David C. James +1
We address the problem of synthetic gene design using Bayesian optimization. The main issue when designing a gene is that the design space is defined in terms of long strings of ch…
Batch Bayesian Optimization via Local Penalization
Javier González, Zhenwen Dai, Philipp Hennig +1
The popularity of Bayesian optimization methods for efficient exploration of parameter spaces has lead to a series of papers applying Gaussian processes as surrogates in the optimi…