no code implementations • 27 Feb 2024 • Arun Kumar A V, Alistair Shilton, Sunil Gupta, Santu Rana, Stewart Greenhill, Svetha Venkatesh
Experimental (design) optimization is a key driver in designing and discovering new products and processes.
no code implementations • 5 Feb 2024 • Dat Phan-Trong, Hung The Tran, Alistair Shilton, Sunil Gupta
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.
no code implementations • 3 Mar 2023 • Sunil Gupta, Alistair Shilton, Arun Kumar A V, Shannon Ryan, Majid Abdolshah, Hung Le, Santu Rana, Julian Berk, Mahad Rashid, Svetha Venkatesh
In this paper we introduce BO-Muse, a new approach to human-AI teaming for the optimization of expensive black-box functions.
no code implementations • 1 Feb 2023 • Alistair Shilton, Sunil Gupta, Santu Rana, Svetha Venkatesh
The study of Neural Tangent Kernels (NTKs) has provided much needed insight into convergence and generalization properties of neural networks in the over-parametrized (wide) limit by approximating the network using a first-order Taylor expansion with respect to its weights in the neighborhood of their initialization values.
1 code implementation • NeurIPS 2021 • Arun Kumar Anjanapura Venkatesh, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh
Traditional methods for kernel selection rely on parametric kernel functions or a combination thereof and although the kernel hyperparameters are tuned, these methods often provide sub-optimal results due to the limitations induced by the parametric forms.
no code implementations • 8 Sep 2020 • Alistair Shilton, Sunil Gupta, Santu Rana, Svetha Venkatesh
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 expected characteristics (length-scale, smoothness, cyclicity etc.)
no code implementations • 15 Jul 2020 • Alistair Shilton, Sunil Gupta, Santu Rana, Svetha Venkatesh
In this paper we explore a connection between deep networks and learning in reproducing kernel Krein space.
1 code implementation • 28 Nov 2019 • Dang Nguyen, Sunil Gupta, Santu Rana, Alistair Shilton, Svetha Venkatesh
To optimize such functions, we propose a new method that formulates the problem as a multi-armed bandit problem, wherein each category corresponds to an arm with its reward distribution centered around the optimum of the objective function in continuous variables.
no code implementations • 9 Sep 2019 • Majid Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh
We introduce a cost-aware multi-objective Bayesian optimisation with non-uniform evaluation cost over objective functions by defining cost-aware constraints over the search space.
no code implementations • 21 Feb 2019 • Alistair Shilton, Sunil Gupta, Santu Rana, Svetha Venkatesh, Majid Abdolshah, Dang Nguyen
In this paper we consider the problem of finding stable maxima of expensive (to evaluate) functions.
no code implementations • NeurIPS 2019 • Majid Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh
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 important than objective B".
no code implementations • 21 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.
no code implementations • 15 Feb 2018 • Cheng Li, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh, Alistair Shilton
Scaling Bayesian optimization to high dimensions is challenging task as the global optimization of high-dimensional acquisition function can be expensive and often infeasible.
no code implementations • 15 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.