Search Results for author: Javier González

Found 17 papers, 6 papers with code

Dynamic Causal Bayesian Optimization

1 code implementation NeurIPS 2021 Virginia Aglietti, Neil Dhir, Javier González, Theodoros Damoulas

This paper studies the problem of performing a sequence of optimal interventions in a causal dynamical system where both the target variable of interest and the inputs evolve over time.

Causal Inference Decision Making

BayesIMP: Uncertainty Quantification for Causal Data Fusion

no code implementations NeurIPS 2021 Siu Lun Chau, Jean-François Ton, Javier González, Yee Whye Teh, Dino Sejdinovic

While causal models are becoming one of the mainstays of machine learning, the problem of uncertainty quantification in causal inference remains challenging.

Bayesian Optimisation Causal Inference

Multi-task Causal Learning with Gaussian Processes

1 code implementation NeurIPS 2020 Virginia Aglietti, Theodoros Damoulas, Mauricio Álvarez, Javier González

This paper studies the problem of learning the correlation structure of a set of intervention functions defined on the directed acyclic graph (DAG) of a causal model.

Active Learning Decision Making +1

Structure Mapping for Transferability of Causal Models

1 code implementation18 Jul 2020 Purva Pruthi, Javier González, Xiaoyu Lu, Madalina Fiterau

Human beings learn causal models and constantly use them to transfer knowledge between similar environments.

reinforcement-learning Transfer Learning

Learning Inconsistent Preferences with Gaussian Processes

no code implementations6 Jun 2020 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 function values.

Gaussian Processes

Causal Bayesian Optimization

no code implementations24 May 2020 Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González

This paper studies the problem of globally optimizing a variable of interest that is part of a causal model in which a sequence of interventions can be performed.

Causal Inference Decision Making

BOFFIN TTS: Few-Shot Speaker Adaptation by Bayesian Optimization

no code implementations4 Feb 2020 Henry B. Moss, Vatsal Aggarwal, Nishant Prateek, Javier González, Roberto Barra-Chicote

We present BOFFIN TTS (Bayesian Optimization For FIne-tuning Neural Text To Speech), a novel approach for few-shot speaker adaptation.

Bandit optimisation of functions in the Matérn kernel RKHS

no code implementations28 Jan 2020 David Janz, David R. Burt, Javier González

We consider the problem of optimising functions in the reproducing kernel Hilbert space (RKHS) of a Mat\'ern kernel with smoothness parameter $\nu$ over the domain $[0, 1]^d$ under noisy bandit feedback.

Deep Gaussian Processes for Multi-fidelity Modeling

1 code implementation18 Mar 2019 Kurt Cutajar, Mark Pullin, Andreas Damianou, Neil Lawrence, Javier González

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 order to construct reliable models.

Decision Making Gaussian Processes +1

Preferential Bayesian Optmization

no code implementations ICML 2017 Javier González, Zhenwen Dai, Andreas Damianou, Neil D. Lawrence

We present a new framework for this scenario that we call Preferential Bayesian Optimization (PBO) and that allows to find the optimum of a latent function that can only be queried through pairwise comparisons, so-called duels.

Recommendation Systems

Bayesian Optimization with Tree-structured Dependencies

no code implementations ICML 2017 Rodolphe Jenatton, Cedric Archambeau, Javier González, Matthias Seeger

The benefit of leveraging this structure is twofold: we explore the search space more efficiently and posterior inference scales more favorably with the number of observations than Gaussian Process-based approaches published in the literature.

Gaussian Processes

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

1 code implementation4 Apr 2017 Eero Siivola, Aki Vehtari, Jarno Vanhatalo, Javier González, Michael Riis Andersen

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 $\mathcal{R}^d$, by using a Gaussian process (GP) as a surrogate model for the objective.

Variational Auto-encoded Deep Gaussian Processes

no code implementations19 Nov 2015 Zhenwen Dai, Andreas Damianou, Javier González, Neil Lawrence

We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model.

Gaussian Processes

GLASSES: Relieving The Myopia Of Bayesian Optimisation

no code implementations21 Oct 2015 Javier González, Michael Osborne, Neil D. Lawrence

We present GLASSES: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search.

Bayesian Optimisation

Bayesian Optimization for Synthetic Gene Design

no code implementations7 May 2015 Javier González, Joseph Longworth, David C. James, Neil D. Lawrence

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

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

no code implementations14 Nov 2013 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 describe dynamical systems.

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