214 citations · 514 across the 18 of their papers we have counts for
20 papers
Invariant Priors for Bayesian Quadrature
Masha Naslidnyk, Javier Gonzalez, Maren Mahsereci
Bayesian quadrature (BQ) is a model-based numerical integration method that is able to increase sample efficiency by encoding and leveraging known structure of the integration task…
Emulation of physical processes with Emukit
Andrei Paleyes, Mark Pullin, Maren Mahsereci +3
Decision making in uncertain scenarios is an ubiquitous challenge in real world systems. Tools to deal with this challenge include simulations to gather information and statistical…
RKHS-SHAP: Shapley Values for Kernel Methods
Siu Lun Chau, Robert Hu, Javier Gonzalez +1
Feature attribution for kernel methods is often heuristic and not individualised for each prediction. To address this, we turn to the concept of Shapley values~(SV), a coalition ga…
GIBBON: General-purpose Information-Based Bayesian OptimisatioN
Henry B. Moss, David S. Leslie, Javier Gonzalez +1
This paper describes a general-purpose extension of max-value entropy search, a popular approach for Bayesian Optimisation (BO). A novel approximation is proposed for the informati…
Good practices for Bayesian Optimization of high dimensional structured spaces
Eero Siivola, Javier Gonzalez, Andrei Paleyes +1
The increasing availability of structured but high dimensional data has opened new opportunities for optimization. One emerging and promising avenue is the exploration of unsupervi…
BOSS: Bayesian Optimization over String Spaces
Henry B. Moss, Daniel Beck, Javier Gonzalez +2
This article develops a Bayesian optimization (BO) method which acts directly over raw strings, proposing the first uses of string kernels and genetic algorithms within BO loops. R…
Learning Inconsistent Preferences with Gaussian Processes
Siu Lun Chau, Javier González, Dino Sejdinovic
We revisit widely used preferential Gaussian processes by Chu et al.(2005) and challenge their modelling assumption that imposes rankability of data items via latent utility functi…
Preferential Batch Bayesian Optimization
Eero Siivola, Akash Kumar Dhaka, Michael Riis Andersen +3
Most research in Bayesian optimization (BO) has focused on \emph{direct feedback} scenarios, where one has access to exact values of some expensive-to-evaluate objective. This dire…
BOFFIN TTS: Few-Shot Speaker Adaptation by Bayesian Optimization
Henry B. Moss, Vatsal Aggarwal, Nishant Prateek +2
We present BOFFIN TTS (Bayesian Optimization For FIne-tuning Neural Text To Speech), a novel approach for few-shot speaker adaptation. Here, the task is to fine-tune a pre-trained…
BINOCULARS for Efficient, Nonmyopic Sequential Experimental Design
Shali Jiang, Henry Chai, Javier Gonzalez +1
Finite-horizon sequential experimental design (SED) arises naturally in many contexts, including hyperparameter tuning in machine learning among more traditional settings. Computin…
Meta-Surrogate Benchmarking for Hyperparameter Optimization
Aaron Klein, Zhenwen Dai, Frank Hutter +2
Despite the recent progress in hyperparameter optimization (HPO), available benchmarks that resemble real-world scenarios consist of a few and very large problem instances that are…
Automatic Discovery of Privacy-Utility Pareto Fronts
Brendan Avent, Javier Gonzalez, Tom Diethe +2
Differential privacy is a mathematical framework for privacy-preserving data analysis. Changing the hyperparameters of a differentially private algorithm allows one to trade off pr…
Deep Gaussian Processes for Multi-fidelity Modeling
Kurt Cutajar, Mark Pullin, Andreas Damianou +2
Multi-fidelity methods are prominently used when cheaply-obtained, but possibly biased and noisy, observations must be effectively combined with limited or expensive true data in o…
Active Multi-Information Source Bayesian Quadrature
Alexandra Gessner, Javier Gonzalez, Maren Mahsereci
Bayesian quadrature (BQ) is a sample-efficient probabilistic numerical method to solve integrals of expensive-to-evaluate black-box functions, yet so far,active BQ learning schemes…
Preferential Bayesian Optimization
Javier Gonzalez, Zhenwen Dai, Andreas Damianou +1
Bayesian optimization (BO) has emerged during the last few years as an effective approach to optimizing black-box functions where direct queries of the objective are expensive. In…
Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations
Eero Siivola, Aki Vehtari, Jarno Vanhatalo +2
Bayesian optimization (BO) is a global optimization strategy designed to find the minimum of an expensive black-box function, typically defined on a compact subset of $\mathcal{R}^…
Variational Auto-encoded Deep Gaussian Processes
Zhenwen Dai, Andreas Damianou, Javier González +1
We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model. Inference is performed in a novel scalable variational fr…
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…
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…
Reproducing kernel Hilbert space based estimation of systems of ordinary differential equations
Javier González, Ivan Vujačić, Ernst Wit
Non-linear systems of differential equations have attracted the interest in fields like system biology, ecology or biochemistry, due to their flexibility and their ability to descr…