Javier Gonzalez | DeepAI

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∙ 04/26/2023

AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires

Recent advances in immunomics have shown that T-cell receptor (TCR) sign...

4 Melanie F. Pradier, et al. ∙

∙ 12/02/2021

Invariant Priors for Bayesian Quadrature

Bayesian quadrature (BQ) is a model-based numerical integration method t...

0 Masha Naslidnyk, et al. ∙

∙ 10/26/2021

Dynamic Causal Bayesian Optimization

This paper studies the problem of performing a sequence of optimal inter...

0 Virginia Aglietti, et al. ∙

∙ 10/25/2021

Emulation of physical processes with Emukit

Decision making in uncertain scenarios is an ubiquitous challenge in rea...

0 Andrei Paleyes, et al. ∙

∙ 10/18/2021

RKHS-SHAP: Shapley Values for Kernel Methods

Feature attribution for kernel methods is often heuristic and not indivi...

0 Siu Lun Chau, et al. ∙

∙ 06/07/2021

BayesIMP: Uncertainty Quantification for Causal Data Fusion

While causal models are becoming one of the mainstays of machine learnin...

0 Siu Lun Chau, et al. ∙

∙ 02/05/2021

GIBBON: General-purpose Information-Based Bayesian OptimisatioN

This paper describes a general-purpose extension of max-value entropy se...

9 Henry B. Moss, et al. ∙

∙ 12/31/2020

Good practices for Bayesian Optimization of high dimensional structured spaces

The increasing availability of structured but high dimensional data has ...

0 Eero Siivola, et al. ∙

∙ 10/02/2020

BOSS: Bayesian Optimization over String Spaces

This article develops a Bayesian optimization (BO) method which acts dir...

51 Henry B. Moss, et al. ∙

∙ 09/27/2020

Multi-task Causal Learning with Gaussian Processes

This paper studies the problem of learning the correlation structure of ...

3 Virginia Aglietti, et al. ∙

∙ 07/18/2020

Structure Mapping for Transferability of Causal Models

Human beings learn causal models and constantly use them to transfer kno...

7 Purva Pruthi, et al. ∙

∙ 06/06/2020

Learning Inconsistent Preferences with Kernel Methods

We propose a probabilistic kernel approach for preferential learning fro...

0 Siu Lun Chau, et al. ∙

∙ 03/25/2020

Preferential Batch Bayesian Optimization

Most research in Bayesian optimization (BO) has focused on direct feedba...

14 Eero Siivola, et al. ∙

∙ 01/28/2020

Bandit optimisation of functions in the Matérn kernel RKHS

We consider the problem of optimising functions in the Reproducing kerne...

0 David Janz, et al. ∙

∙ 09/10/2019

Efficient nonmyopic Bayesian optimization and quadrature

Finite-horizon sequential decision problems arise naturally in many mach...

0 Shali Jiang, et al. ∙

∙ 05/30/2019

Meta-Surrogate Benchmarking for Hyperparameter Optimization

Despite the recent progress in hyperparameter optimization (HPO), availa...

0 Aaron Klein, et al. ∙

∙ 05/26/2019

Automatic Discovery of Privacy-Utility Pareto Fronts

Differential privacy is a mathematical framework for privacy-preserving ...

0 Brendan Avent, et al. ∙

∙ 03/27/2019

Active Multi-Information Source Bayesian Quadrature

Bayesian quadrature (BQ) is a sample-efficient probabilistic numerical m...

0 Alexandra Gessner, et al. ∙

∙ 03/18/2019

Deep Gaussian Processes for Multi-fidelity Modeling

Multi-fidelity methods are prominently used when cheaply-obtained, but p...

4 Kurt Cutajar, et al. ∙

∙ 04/12/2017

Preferential Bayesian Optimization

Bayesian optimization (BO) has emerged during the last few years as an e...

0 Javier Gonzalez, et al. ∙

∙ 04/04/2017

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

Bayesian optimization () is a global optimization strategy designed to f...

0 Eero Siivola, et al. ∙

∙ 11/19/2015

Variational Auto-encoded Deep Gaussian Processes

We develop a scalable deep non-parametric generative model by augmenting...

0 Zhenwen Dai, et al. ∙

∙ 10/21/2015

GLASSES: Relieving The Myopia Of Bayesian Optimisation

We present GLASSES: Global optimisation with Look-Ahead through Stochast...

0 Javier Gonzalez, et al. ∙

∙ 05/29/2015

Batch Bayesian Optimization via Local Penalization

The popularity of Bayesian optimization methods for efficient exploratio...

0 Javier Gonzalez, et al. ∙

∙ 05/07/2015

Bayesian Optimization for Synthetic Gene Design

We address the problem of synthetic gene design using Bayesian optimizat...

0 Javier Gonzalez, et al. ∙

∙ 11/14/2013

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

Non-linear systems of differential equations have attracted the interest...

0 Javier Gonzalez, et al. ∙

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