Bayesian Optimisation
86 papers with code • 0 benchmarks • 0 datasets
Expensive black-box functions are a common problem in many disciplines, including tuning the parameters of machine learning algorithms, robotics, and other engineering design problems. Bayesian Optimisation is a principled and efficient technique for the global optimisation of these functions. The idea behind Bayesian Optimisation is to place a prior distribution over the target function and then update that prior with a set of “true” observations of the target function by expensively evaluating it in order to produce a posterior predictive distribution. The posterior then informs where to make the next observation of the target function through the use of an acquisition function, which balances the exploitation of regions known to have good performance with the exploration of regions where there is little information about the function’s response.
Source: A Bayesian Approach for the Robust Optimisation of Expensive-to-Evaluate Functions
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Latest papers with no code
Shaping of Magnetic Field Coils in Fusion Reactors using Bayesian Optimisation
The optimisation helps to identify design parameters that would minimise the costs incurred while maximising the plasma stability by way of minimising magnetic ripples.
Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence
In this study, we propose a novel approach for predicting time-series data using GNN and monitoring with Reinforcement Learning (RL).
Optimal Observation-Intervention Trade-Off in Optimisation Problems with Causal Structure
We give theoretical results detailing the structure of the optimal stopping times and demonstrate the generality of our approach by showing that it can be integrated with existing causal Bayesian optimisation algorithms.
Will More Expressive Graph Neural Networks do Better on Generative Tasks?
Moreover, we show that GCPN and GraphAF with advanced GNNs can achieve state-of-the-art results across 17 other non-GNN-based graph generative approaches, such as variational autoencoders and Bayesian optimisation models, on the proposed molecular generative objectives (DRD2, Median1, Median2), which are impor- tant metrics for de-novo molecular design.
Machine Learning-Assisted Discovery of Novel Reactor Designs
To address this challenge, we establish a machine learning-assisted approach for the design of the next-generation of chemical reactors, combining the application of high-dimensional parameterisations, computational fluid dynamics, and multi-fidelity Bayesian optimisation.
Bayesian Optimisation of Functions on Graphs
The increasing availability of graph-structured data motivates the task of optimising over functions defined on the node set of graphs.
Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI
To expand the application of Green AI, we advocate for a shift in the design of deep learning models, by considering the trade-off between energy efficiency and accuracy.
Automated control and optimisation of laser driven ion acceleration
The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space.
MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning
In Bayesian optimisation, we often seek to minimise the black-box objective functions that arise in real-world physical systems.
Delayed Feedback in Kernel Bandits
An abstraction of the problem can be formulated as a kernel based bandit problem (also known as Bayesian optimisation), where a learner aims at optimising a kernelized function through sequential noisy observations.
