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03/03/2023
BO-Muse: A human expert and AI teaming framework for accelerated experimental design
In this paper we introduce BO-Muse, a new approach to human-AI teaming f...
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02/01/2023
Gradient Descent in Neural Networks as Sequential Learning in RKBS
The study of Neural Tangent Kernels (NTKs) has provided much needed insi...
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09/08/2020
Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition
We propose an algorithm for Bayesian functional optimisation - that is, ...
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07/15/2020
From deep to Shallow: Equivalent Forms of Deep Networks in Reproducing Kernel Krein Space and Indefinite Support Vector Machines
In this paper we explore a connection between deep networks and learning...
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11/28/2019
Bayesian Optimization for Categorical and Category-Specific Continuous Inputs
Many real-world functions are defined over both categorical and category...
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09/09/2019
Cost-aware Multi-objective Bayesian optimisation
The notion of expense in Bayesian optimisation generally refers to the u...
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02/21/2019
Stable Bayesian Optimisation via Direct Stability Quantification
In this paper we consider the problem of finding stable maxima of expens...
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02/12/2019
Multi-objective Bayesian optimisation with preferences over objectives
We present a Bayesian multi-objective optimisation algorithm that allows...
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05/21/2018
Kernel Pre-Training in Feature Space via m-Kernels
This paper presents a novel approach to kernel tuning. The method presen...
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02/15/2018
High Dimensional Bayesian Optimization Using Dropout
Scaling Bayesian optimization to high dimensions is challenging task as ...
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02/15/2018