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Showing 1 to 20 of 25 for “"Bayesian optimisation"”.
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Volatility Model Pricing and Calibration with Neural Networks using Bayesian Optimisation
… the indirect method implements a least squares optimisation algorithm to calibrate the parameters. The direct method, on the other hand, uses a deep artificial neural network to calibrate the model parameters using the implied volatility surface as input. Bayesian Optimisation algorithms are …
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Bayesian Optimisation of Hyperparameters in Regression Models for Smart Energy and Environmental Systems
… The results demonstrate that hyperparameter optimisation significantly improves predictive performance and computational efficiency. For example, optimisation of artificial neural network models improved prediction accuracy for smart home energy consumption from 60\% to approximately 85\%, …
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Probabilistic machine learning algorithms for molecule discovery
… learning, this approach is typically called Bayesian optimisation and has been studied for many other problems, such as tuning hyperparameters of machine learning models. Although in principle Bayesian optimisation can be straightforwardly applied to the problem of discovering new molecules, …
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Bayesian single- and multi- objective optimisation with nonparametric priors
Optimisation is integral to all sorts of processes in science, economics and arguably underpins the fruition of human intelligence through millions of years of optimisation, or $\textit{evolution}$. Scarce resources make it crucial to maximise their efficient usage. In this thesis, we consider the …
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Optimising the Optimiser: Meta NeuroEvolution for Artificial Intelligence Problems
… were investigated. In the first two experiments, Bayesian Optimisation and random search are compared. In the third and final experiment, the hyperparameter values found in second experiment are used to solve a more difficult reinforcement learning task, effectively performing hyperparameter …
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Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes
… GPs are currently the workhorse model for Bayesian optimisation, a methodology foreseen to be a vehicle for guiding laboratory experiments in scientific discovery campaigns. The first contribution of this thesis is to use GP modelling to reason about the latent emission signature from the …
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Statistical Surrogate Models for Robust Design Optimisation in Reduced Dimension
… crucial for multi-query problems such as design optimisation. However, the design of these products depends on complex and often deterministic computational models that may be expensive-to-evaluate. Consequently, it is expedient to consider these models as black-box functions, such that they are …
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Bayesian robust optimisation of buckling loads of trusses with random imperfections
Structural optimisation with linear responses is a highly active research field with broad applications. In practice, nonlinear structural behaviour and random imperfections different from the ideal design often need to be considered. Optimised lightweight structures, such as truss-based shallow …
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Automatic and adaptive preprocessing for the development of predictive models.
… an approach for automating the selection and optimisation of multiple preprocessing methods and predictors has been proposed. The combination of multiple data mining methods forming a workflow is known as Multi-Component Predictive System (MCPS). There are multiple software platforms like Weka …
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Algorithmic Approaches for Context-Informed Reaction Prediction
… (UDM) can be used as a language for closed-loop optimisation. UDM is a standard for storage of chemical data, much like the Open Reaction Database (ORD). The ORD contains millions of reactions, and to prepare machine learning datasets from these reactions, I present the Python package ORDerly. …
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Optimisation of Gaussian process regressions of molecular potential energy surfaces
… creates instability in the models that a Bayesian approach to Gaussian processes creates and the expression of the training data on the feature space is thus important. This thesis covers three aspects of the learning process. Firstly, Gaussian processes that project the input molecular …
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Scalable Gaussian Processes: Advances in Iterative Methods and Pathwise Conditioning
… linear systems iteratively. To this end, custom optimisation objectives, stochastic gradient estimators, and variance reduction techniques are developed and analysed. Empirically, the proposed methods achieve state-of-the-art performance on large- scale regression, Bayesian optimisation, and …
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Relative Position Control for Satellite Formation Flying
… tuning strategy was developed using Bayesian optimisation to improve controller performance. Seven hyperparameters were tuned using a unique algorithm that penalised satellite crashes and rewarded lowrelative positional error between the satellites. The algorithm was integrated …
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Bayesian Learning for Data-Efficient Control
… especially small datasets. We use probabilistic Bayesian modelling to learn systems from scratch, similar to the PILCO algorithm, which achieved unprecedented data efficiency in learning control of several benchmarks. We extend PILCO in three principle ways. First, we learn control under …
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SPDE-derived random fields in structural optimisation and elastodynamics
… variability with applications on structural optimisation and statistical inference. Gaussian random fields on lattice structures are modelled by exploiting the established connection between random fields and stochastic partial differential equations (SPDEs). For a random field with Matérn …
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Automated Machine Learning for Predicting Trends in Time Series Data
… the Algorithm Selection and Hyperparameter Optimisation (ASHO) is performed manually. However, manual ASHO is expensive and often results in a sub-optimal or mediocre model because it needs extensive experimentation as well as domain specific and Machine Learning (ML) expert knowledge. This …
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Machine-assisted synthesis and development in pharmaceutical industry
… challenges. First, we explored use of black-box Bayesian optimisation algorithm TS-EMO for optimisation of complex reaction networks, such as the bio-waste crude sulphate turpentine conversion to functional molecules, with no prior mechanistic information. Using Gaussian processes as surrogate …
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Optimizing Data-Intensive Computing with Efficient Configuration Tuning
… hundreds of execution samples). Recently, Bayesian Optimisation (BO) strategies have been applied as a solution to enable efficient autotuning. They build a probabilistic model incrementally to predict the impact of the parameters on performance using a small number of execution samples. …
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Onshore wind farm battery energy storage systems optimisation
… advanced methodologies such as intelligent optimisation, hybrid forecasting models, and complex algorithms, the research provides innovative solutions for enhancing grid integration and energy management. The study aligns with the United Nations Sustainable Development Goals (SDGs), …
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Slice-selective parallel transmit magnetic resonance imaging at 7T
… its application to 2D imaging suffers from optimisation complexities, scanner integration hurdles, and specific absorption rate (SAR) constraints, hindering translation into routine clinical use. The overarching aim of my PhD is to develop fast and robust pulse design methods for 2D pTx …
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