23 citations · 59 across the 15 of their papers we have counts for
15 papers
Novel Kernel Models and Exact Representor Theory for Neural Networks Beyond the Over-Parameterized Regime
Alistair Shilton, Sunil Gupta, Santu Rana +1
This paper presents two models of neural-networks and their training applicable to neural networks of arbitrary width, depth and topology, assuming only finite-energy neural activa…
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…
Gradient Descent in Neural Networks as Sequential Learning in RKBS
Alistair Shilton, Sunil Gupta, Santu Rana +1
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…
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…
Accelerated Bayesian Optimization throughWeight-Prior Tuning
Alistair Shilton, Sunil Gupta, Santu Rana +10
Bayesian optimization (BO) is a widely-used method for optimizing expensive (to evaluate) problems. At the core of most BO methods is the modeling of the objective function using a…
Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation
Alistair Shilton, Sunil Gupta, Santu Rana +9
The paper presents a novel approach to direct covariance function learning for Bayesian optimisation, with particular emphasis on experimental design problems where an existing cor…
High Dimensional Bayesian Optimization Using Dropout
Cheng Li, Sunil Gupta, Santu Rana +3
Scaling Bayesian optimization to high dimensions is challenging task as the global optimization of high-dimensional acquisition function can be expensive and often infeasible. Exis…
Fast supersymmetry phenomenology at the Large Hadron Collider using machine learning techniques
A. Buckley, A. Shilton, M. J. White
A pressing problem for supersymmetry (SUSY) phenomenologists is how to incorporate Large Hadron Collider search results into parameter fits designed to measure or constrain the SUS…