Javier Gonzalez

BayesIMP: Uncertainty Quantification for Causal Data Fusion

While causal models are becoming one of the mainstays of machine learnin...
06/07/2021 ∙ by Siu Lun Chau, et al. ∙ 0 ∙ share

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GIBBON: General-purpose Information-Based Bayesian OptimisatioN

This paper describes a general-purpose extension of max-value entropy se...
02/05/2021 ∙ by Henry B. Moss, et al. ∙ 9 ∙ share

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Good practices for Bayesian Optimization of high dimensional structured spaces

The increasing availability of structured but high dimensional data has ...
12/31/2020 ∙ by Eero Siivola, et al. ∙ 0 ∙ share

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BOSS: Bayesian Optimization over String Spaces

This article develops a Bayesian optimization (BO) method which acts dir...
10/02/2020 ∙ by Henry B. Moss, et al. ∙ 51 ∙ share

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Multi-task Causal Learning with Gaussian Processes

This paper studies the problem of learning the correlation structure of ...
09/27/2020 ∙ by Virginia Aglietti, et al. ∙ 3 ∙ share

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Structure Mapping for Transferability of Causal Models

Human beings learn causal models and constantly use them to transfer kno...
07/18/2020 ∙ by Purva Pruthi, et al. ∙ 7 ∙ share

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Learning Inconsistent Preferences with Kernel Methods

We propose a probabilistic kernel approach for preferential learning fro...
06/06/2020 ∙ by Siu Lun Chau, et al. ∙ 0 ∙ share

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Preferential Batch Bayesian Optimization

Most research in Bayesian optimization (BO) has focused on direct feedba...
03/25/2020 ∙ by Eero Siivola, et al. ∙ 14 ∙ share

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Bandit optimisation of functions in the Matérn kernel RKHS

We consider the problem of optimising functions in the Reproducing kerne...
01/28/2020 ∙ by David Janz, et al. ∙ 0 ∙ share

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Efficient nonmyopic Bayesian optimization and quadrature

Finite-horizon sequential decision problems arise naturally in many mach...
09/10/2019 ∙ by Shali Jiang, et al. ∙ 0 ∙ share

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Meta-Surrogate Benchmarking for Hyperparameter Optimization

Despite the recent progress in hyperparameter optimization (HPO), availa...
05/30/2019 ∙ by Aaron Klein, et al. ∙ 0 ∙ share

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Automatic Discovery of Privacy-Utility Pareto Fronts

Differential privacy is a mathematical framework for privacy-preserving ...
05/26/2019 ∙ by Brendan Avent, et al. ∙ 0 ∙ share

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Active Multi-Information Source Bayesian Quadrature

Bayesian quadrature (BQ) is a sample-efficient probabilistic numerical m...
03/27/2019 ∙ by Alexandra Gessner, et al. ∙ 0 ∙ share

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Deep Gaussian Processes for Multi-fidelity Modeling

Multi-fidelity methods are prominently used when cheaply-obtained, but p...
03/18/2019 ∙ by Kurt Cutajar, et al. ∙ 4 ∙ share

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Preferential Bayesian Optimization

Bayesian optimization (BO) has emerged during the last few years as an e...
04/12/2017 ∙ by Javier Gonzalez, et al. ∙ 0 ∙ share

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Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations

Bayesian optimization () is a global optimization strategy designed to f...
04/04/2017 ∙ by Eero Siivola, et al. ∙ 0 ∙ share

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Variational Auto-encoded Deep Gaussian Processes

We develop a scalable deep non-parametric generative model by augmenting...
11/19/2015 ∙ by Zhenwen Dai, et al. ∙ 0 ∙ share

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GLASSES: Relieving The Myopia Of Bayesian Optimisation

We present GLASSES: Global optimisation with Look-Ahead through Stochast...
10/21/2015 ∙ by Javier Gonzalez, et al. ∙ 0 ∙ share

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Batch Bayesian Optimization via Local Penalization

The popularity of Bayesian optimization methods for efficient exploratio...
05/29/2015 ∙ by Javier Gonzalez, et al. ∙ 0 ∙ share

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Bayesian Optimization for Synthetic Gene Design

We address the problem of synthetic gene design using Bayesian optimizat...
05/07/2015 ∙ by Javier Gonzalez, et al. ∙ 0 ∙ share

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Reproducing kernel Hilbert space based estimation of systems of ordinary differential equations

Non-linear systems of differential equations have attracted the interest...
11/14/2013 ∙ by Javier Gonzalez, et al. ∙ 0 ∙ share

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Javier Gonzalez

Featured Co-authors

Yee Whye Teh

Frank Hutter

Philipp Hennig

Aki Vehtari

Dino Sejdinovic

Neil D. Lawrence

Andreas Damianou

Zhenwen Dai

David S. Leslie

Borja Balle

Roman Garnett

BayesIMP: Uncertainty Quantification for Causal Data Fusion

GIBBON: General-purpose Information-Based Bayesian OptimisatioN

Good practices for Bayesian Optimization of high dimensional structured spaces

BOSS: Bayesian Optimization over String Spaces

Multi-task Causal Learning with Gaussian Processes

Structure Mapping for Transferability of Causal Models

Learning Inconsistent Preferences with Kernel Methods

Preferential Batch Bayesian Optimization

Bandit optimisation of functions in the Matérn kernel RKHS

Efficient nonmyopic Bayesian optimization and quadrature

Meta-Surrogate Benchmarking for Hyperparameter Optimization

Automatic Discovery of Privacy-Utility Pareto Fronts

Active Multi-Information Source Bayesian Quadrature

Deep Gaussian Processes for Multi-fidelity Modeling

Preferential Bayesian Optimization

Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations

Variational Auto-encoded Deep Gaussian Processes

GLASSES: Relieving The Myopia Of Bayesian Optimisation

Batch Bayesian Optimization via Local Penalization

Bayesian Optimization for Synthetic Gene Design

Reproducing kernel Hilbert space based estimation of systems of ordinary differential equations