Bayesian Optimisation
84 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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Use these libraries to find Bayesian Optimisation models and implementationsLatest papers with no code
Personalized LLM Response Generation with Parameterized Memory Injection
Large Language Models (LLMs) have exhibited remarkable proficiency in comprehending and generating natural language.
Single and Multi-Objective Real-Time Optimisation of an Industrial Injection Moulding Process via a Bayesian Adaptive Design of Experiment Approach
In this study, for the first time, an experimental ADoE approach, based on Bayesian optimisation, was developed in injection moulding using process and sensor data to optimise the quality and cycle time in real-time.
Beyond Lengthscales: No-regret Bayesian Optimisation With Unknown Hyperparameters Of Any Type
Previously proposed algorithms with the no-regret property were only able to handle the special case of unknown lengthscales, reproducing kernel Hilbert space norm and applied only to the frequentist case.
Long-run Behaviour of Multi-fidelity Bayesian Optimisation
Multi-fidelity Bayesian Optimisation (MFBO) has been shown to generally converge faster than single-fidelity Bayesian Optimisation (SFBO) (Poloczek et al. (2017)).
High-Dimensional Bayesian Optimisation with Large-Scale Constraints -- An Application to Aeroelastic Tailoring
Due to the high number of design variables and constraints, these problems are ordinarily solved using gradient-based optimisation methods, leading to a local solution in the design space while the global space is neglected.
Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms
We highlight that existing procedures that only use the expected return are limited on two fronts: first an infinite number of return distributions with a wide range of performance-reproducibility trade-offs can have the same expected return, limiting its effectiveness when used for comparing policies; second, the expected return metric does not leave any room for practitioners to choose the best trade-off value for considered applications.
Search Strategies for Self-driving Laboratories with Pending Experiments
To minimize station downtime and maximize experimental throughput, it is practical to run experiments in asynchronous parallel, in which multiple experiments are being performed at once in different stages.
Impact of HPO on AutoML Forecasting Ensembles
This paper delves into the aspect of adding different hyperparameter optimization strategies to the deep learning models in such a setup (DeepAR and MQ-CNN), exploring the trade-off between added training cost and the increase in accuracy for different configurations.
Multi-fidelity Bayesian Optimisation of Syngas Fermentation Simulators
A Bayesian optimization approach for maximizing the gas conversion rate in an industrial-scale bioreactor for syngas fermentation is presented.
Robust and Conjugate Gaussian Process Regression
To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed Gaussian observation noise.