3 citations · 6 across the 7 of their papers we have counts for
7 papers · 1 filter
Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties
Arun Kumar A, Alistair Shilton, Sunil Gupta +3
Experimental (design) optimization is a key driver in designing and discovering new products and processes. Bayesian Optimization (BO) is an effective tool for optimizing expensive…
PINN-BO: A Black-box Optimization Algorithm using Physics-Informed Neural Networks
Dat Phan-Trong, Hung The Tran, Alistair Shilton +1
Black-box optimization is a powerful approach for discovering global optima in noisy and expensive black-box functions, a problem widely encountered in real-world scenarios. Recent…
BO-Muse: A human expert and AI teaming framework for accelerated experimental design
Sunil Gupta, Alistair Shilton, Arun Kumar A +7
In this paper we introduce BO-Muse, a new approach to human-AI teaming for the optimization of expensive black-box functions. Inspired by the intrinsic difficulty of extracting exp…
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…
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…