Search Results for author: Murray Height

Found 6 papers, 0 papers with code

Accelerating Experimental Design by Incorporating Experimenter Hunches

no code implementations22 Jul 2019 Cheng Li, Santu Rana, Sunil Gupta, Vu Nguyen, Svetha Venkatesh, Alessandra Sutti, David Rubin, Teo Slezak, Murray Height, Mazher Mohammed, Ian Gibson

In this paper, we consider per-variable monotonic trend in the underlying property that results in a unimodal trend in those variables for a target value optimization.

Bayesian Optimization Experimental Design

Bayesian functional optimisation with shape prior

no code implementations19 Sep 2018 Pratibha Vellanki, Santu Rana, Sunil Gupta, David Rubin de Celis Leal, Alessandra Sutti, Murray Height, Svetha Venkatesh

Real world experiments are expensive, and thus it is important to reach a target in minimum number of experiments.

Bayesian Optimisation

Accelerated Bayesian Optimization throughWeight-Prior Tuning

no code implementations21 May 2018 Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Laurence Park, Cheng Li, Svetha Venkatesh, Alessandra Sutti, David Rubin, Thomas Dorin, Alireza Vahid, Murray Height, Teo Slezak

In this paper we show how such auxiliary data may be used to construct a GP covariance corresponding to a more appropriate weight prior for the objective function.

Transfer Learning

Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation

no code implementations15 Feb 2018 Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Cheng Li, Laurence Park, Svetha Venkatesh, Alessandra Sutti, David Rubin, Thomas Dorin, Alireza Vahid, Murray Height

The paper presents a novel approach to direct covariance function learning for Bayesian optimisation, with particular emphasis on experimental design problems where an existing corpus of condensed knowledge is present.

Bayesian Optimisation Experimental Design

Process-constrained batch Bayesian optimisation

no code implementations NeurIPS 2017 Pratibha Vellanki, Santu Rana, Sunil Gupta, David Rubin, Alessandra Sutti, Thomas Dorin, Murray Height, Paul Sanders, Svetha Venkatesh

We demonstrate the performance of both pc-BO(basic) and pc-BO(nested) by optimising benchmark test functions, tuning hyper-parameters of the SVM classifier, optimising the heat-treatment process for an Al-Sc alloy to achieve target hardness, and optimising the short polymer fibre production process.

Bayesian Optimisation

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