5 citations · 12 across the 6 of their papers we have counts for
8 papers
Accelerating Multi-Objective Bayesian Optimisation via Predictive-Gradient Catalysts
Alma Rahat, Tinkle Chugh, Jonathan Fieldsend +1
This paper presents a general acceleration mechanism for multi-objective Bayesian optimisation (MOBO) that leverages Gaussian process predictive gradients as auxiliary signals. Rat…
Mono-surrogate vs Multi-surrogate in Multi-objective Bayesian Optimisation
Tinkle Chugh
Bayesian optimisation (BO) has been widely used to solve problems with expensive function evaluations. In multi-objective optimisation problems, BO aims to find a set of approximat…
Efficient Approximation of Expected Hypervolume Improvement using Gauss-Hermite Quadrature
Alma Rahat, Tinkle Chugh, Jonathan Fieldsend +2
Many methods for performing multi-objective optimisation of computationally expensive problems have been proposed recently. Typically, a probabilistic surrogate for each objective…
R-MBO: A Multi-surrogate Approach for Preference Incorporation in Multi-objective Bayesian Optimisation
Tinkle Chugh
Many real-world multi-objective optimisation problems rely on computationally expensive function evaluations. Multi-objective Bayesian optimisation (BO) can be used to alleviate th…
Wind Farm Layout Optimisation using Set Based Multi-objective Bayesian Optimisation
Tinkle Chugh, Endi Ymeraj
Wind energy is one of the cleanest renewable electricity sources and can help in addressing the challenge of climate change. One of the drawbacks of wind-generated energy is the la…
MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimation
George De Ath, Tinkle Chugh, Alma A. M. Rahat
Optimisation problems often have multiple conflicting objectives that can be computationally and/or financially expensive. Mono-surrogate Bayesian optimisation (BO) is a popular mo…
What Makes an Effective Scalarising Function for Multi-Objective Bayesian Optimisation?
Clym Stock-Williams, Tinkle Chugh, Alma Rahat +1
Performing multi-objective Bayesian optimisation by scalarising the objectives avoids the computation of expensive multi-dimensional integral-based acquisition functions, instead o…
Scalarizing Functions in Bayesian Multiobjective Optimization
Tinkle Chugh
Scalarizing functions have been widely used to convert a multiobjective optimization problem into a single objective optimization problem. However, their use in solving (computatio…