ϵ-shotgun: ϵ-greedy batch bayesian optimisation

Bayesian optimisation is a popular surrogate model-based approach for optimising expensive black-box functions. Given a surrogate model, the next location to expensively evaluate is chosen via maximisation of a cheap-to-query acquisition function. We present an ϵ-greedy procedure for Bayesian optimisation in batch settings in which the black-box function can be evaluated multiple times in parallel. Our ϵ-shotgun algorithm leverages the model's prediction, uncertainty, and the approximated rate of change of the landscape to determine the spread of batch solutions to be distributed around a putative location. The initial target location is selected either in an exploitative fashion on the mean prediction, or - with probability ϵ - from elsewhere in the design space. This results in locations that are more densely sampled in regions where the function is changing rapidly and in locations predicted to be good (i.e. close to predicted optima), with more scattered samples in regions where the function is flatter and/or of poorer quality. We empirically evaluate the ϵ-shotgun methods on a range of synthetic functions and two real-world problems, finding that they perform at least as well as state-of-the-art batch methods and in many cases exceed their performance

What do you mean? The role of the mean function in Bayesian optimisation.

This is the author accepted manuscriptBayesian optimisation is a popular approach for optimising expensive black-box functions. The next location to be evaluated is selected via maximising an acquisition function that balances exploitation and exploration. Gaussian processes, the surrogate models of choice in Bayesian optimisation, are often used with a constant prior mean function equal to the arithmetic mean of the observed function values. We show that the rate of convergence can depend sensitively on the choice of mean function. We empirically investigate 8 mean functions (constant functions equal to the arithmetic mean, minimum, median and maximum of the observed function evaluations, linear, quadratic polynomials, random forests and RBF networks), using 10 synthetic test problems and two real-world problems, and using the Expected Improvement and Upper Confidence Bound acquisition functions. We find that for design dimensions ≥5 using a constant mean function equal to the worst observed quality value is consistently the best choice on the synthetic problems considered. We argue that this worst-observed-quality function promotes exploitation leading to more rapid convergence. However, for the real-world tasks the more complex mean functions capable of modelling the fitness landscape may be effective, although there is no clearly optimum optimum choice.Innovate U

An Equine Herpesvirus Type 1 (EHV-1) Expressing VP2 and VP5 of Serotype 8 Bluetongue Virus (BTV-8) Induces Protection in a Murine Infection Model

Bluetongue virus (BTV) can infect most species of domestic and wild ruminants causing substantial morbidity and mortality and, consequently, high economic losses. In 2006, an epizootic of BTV serotype 8 (BTV-8) started in northern Europe that caused significant disease in cattle and sheep before comprehensive vaccination was introduced two years later. Here, we evaluate the potential of equine herpesvirus type 1 (EHV-1), an alphaherpesvirus, as a novel vectored DIVA (differentiating infected from vaccinated animals) vaccine expressing VP2 of BTV-8 alone or in combination with VP5. The EHV-1 recombinant viruses stably expressed the transgenes and grew with kinetics that were identical to those of parental virus in vitro. After immunization of mice, a BTV-8-specific neutralizing antibody response was elicited. In a challenge experiment using a lethal dose of BTV-8, 100% of interferon-receptor-deficient (IFNAR−/−) mice vaccinated with the recombinant EHV-1 carrying both VP2 and VP5, but not VP2 alone, survived. VP7 was not included in the vectored vaccines and was successfully used as a DIVA marker. In summary, we show that EHV-1 expressing BTV-8 VP2 and VP5 is capable of eliciting a protective immune response that is distinguishable from that after infection and as such may be an alternative for BTV vaccination strategies in which DIVA compatibility is of importance