2 citations · 2 across the 4 of their papers we have counts for
9 papers
Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition
Alistair Shilton, Sunil Gupta, Santu Rana +1
We propose an algorithm for Bayesian functional optimisation - that is, finding the function to optimise a process - guided by experimenter beliefs and intuitions regarding the exp…
From deep to Shallow: Equivalent Forms of Deep Networks in Reproducing Kernel Krein Space and Indefinite Support Vector Machines
Alistair Shilton, Sunil Gupta, Santu Rana +1
In this paper we explore a connection between deep networks and learning in reproducing kernel Krein space. Our approach is based on the concept of push-forward - that is, taking a…
Bayesian Optimization for Categorical and Category-Specific Continuous Inputs
Dang Nguyen, Sunil Gupta, Santu Rana +2
Many real-world functions are defined over both categorical and category-specific continuous variables and thus cannot be optimized by traditional Bayesian optimization (BO) method…
Cost-aware Multi-objective Bayesian optimisation
Majid Abdolshah, Alistair Shilton, Santu Rana +2
The notion of expense in Bayesian optimisation generally refers to the uniformly expensive cost of function evaluations over the whole search space. However, in some scenarios, the…
Stable Bayesian Optimisation via Direct Stability Quantification
Alistair Shilton, Sunil Gupta, Santu Rana +3
In this paper we consider the problem of finding stable maxima of expensive (to evaluate) functions. We are motivated by the optimisation of physical and industrial processes where…
Multi-objective Bayesian optimisation with preferences over objectives
Majid Abdolshah, Alistair Shilton, Santu Rana +2
We present a multi-objective Bayesian optimisation algorithm that allows the user to express preference-order constraints on the objectives of the type "objective A is more importa…