Papers Bayesian Optimisation
“Bayesian Optimisation” 태그가 달린 논문 221편 · 필터 해제
Some variation of COBRA in sequential learning setup
This research paper introduces innovative approaches for multivariate time series forecasting based on different variations of the combined regression strategy. We use specific data preprocessing techniques which makes a…
Bayesian OptimisationLoad ForecastingMultivariate Time Series ForecastingTime Series+1Personalized LLM Response Generation with Parameterized Memory Injection
Large Language Models (LLMs) have exhibited remarkable proficiency in comprehending and generating natural language. On the other hand, personalized LLM response generation holds the potential to offer substantial benefi…
Bayesian Optimisationparameter-efficient fine-tuningResponse GenerationSingle and Multi-Objective Real-Time Optimisation of an Industrial Injection Moulding Process via a Bayesian Adaptive Design of Experiment Approach
Minimising cycle time without inducing quality defects is a major challenge in the injection moulding (IM). Design of Experiment methods (DoE) have been widely studied for optimisation of the IM, however existing methods…
Bayesian OptimisationOn the development of a practical Bayesian optimisation algorithm for expensive experiments and simulations with changing environmental conditions
Experiments in engineering are typically conducted in controlled environments where parameters can be set to any desired value. This assumes that the same applies in a real-world setting -- an assumption that is often in…
Bayesian OptimisationTime-Varying Gaussian Process Bandits with Unknown Prior
Bayesian optimisation requires fitting a Gaussian process model, which in turn requires specifying prior on the unknown black-box function -- most of the theoretical literature assumes this prior is known. However, it is…
Bayesian OptimisationAutomated Machine Learning for Positive-Unlabelled Learning
Positive-Unlabelled (PU) learning is a growing field of machine learning that aims to learn classifiers from data consisting of labelled positive and unlabelled instances, which can be in reality positive or negative, bu…
Bayesian OptimisationCheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations
Machine learning has emerged as a powerful solution to the modern challenges in accelerator physics. However, the limited availability of beam time, the computational cost of simulations, and the high-dimensionality of o…
Bayesian OptimisationLong-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)). Inspired by recent benchmark papers, we are investigatin…
Bayesian OptimisationHigh-Dimensional Bayesian Optimisation with Large-Scale Constraints -- An Application to Aeroelastic Tailoring
Design optimisation potentially leads to lightweight aircraft structures with lower environmental impact. Due to the high number of design variables and constraints, these problems are ordinarily solved using gradient-ba…
Bayesian OptimisationDimensionality ReductionBeyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms
Many applications in Reinforcement Learning (RL) usually have noise or stochasticity present in the environment. Beyond their impact on learning, these uncertainties lead the exact same policy to perform differently, i.e…
Bayesian OptimisationReinforcement Learning (RL)Search Strategies for Self-driving Laboratories with Pending Experiments
Self-driving laboratories (SDLs) consist of multiple stations that perform material synthesis and characterisation tasks. To minimize station downtime and maximize experimental throughput, it is practical to run experime…
Bayesian OptimisationExpert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known Systems
Domain experts often possess valuable physical insights that are overlooked in fully automated decision-making processes such as Bayesian optimisation. In this article we apply high-throughput (batch) Bayesian optimisati…
Bayesian OptimisationDecision MakingExperimental DesignData-driven Prior Learning for Bayesian Optimisation
Transfer learning for Bayesian optimisation has generally assumed a strong similarity between optimisation tasks, with at least a subset having similar optimal inputs. This assumption can reduce computational costs, but …
Bayesian OptimisationTransfer LearningImpact of HPO on AutoML Forecasting Ensembles
A forecasting ensemble consisting of a diverse range of estimators for both local and global univariate forecasting, in particular MQ-CNN,DeepAR, Prophet, NPTS, ARIMA and ETS, can be used to make forecasts for a variety …
AutoMLAvgBayesian OptimisationEnsemble Learning+1Multi-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. We have access to a high-fidelity, computational fluid dynamic (CFD) reactor…
Bayesian OptimisationBayesian OptimizationRobust 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. This strong and simplistic assumption is often violated i…
Bayesian InferenceBayesian OptimisationFormGaussian Processes+2Stochastic Gradient Descent for Gaussian Processes Done Right
As is well known, both sampling from the posterior and computing the mean of the posterior in Gaussian process regression reduces to solving a large linear system of equations. We study the use of stochastic gradient des…
Bayesian OptimisationGaussian ProcessesregressionShaping of Magnetic Field Coils in Fusion Reactors using Bayesian Optimisation
Nuclear fusion using magnetic confinement holds promise as a viable method for sustainable energy. However, most fusion devices have been experimental and as we move towards energy reactors, we are entering into a new pa…
Bayesian OptimisationGraph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence
Reinforcement learning is well known for its ability to model sequential tasks and learn latent data patterns adaptively. Deep learning models have been widely explored and adopted in regression and classification tasks.…
Bayesian OptimisationDeep Learningreinforcement-learningReinforcement Learning (RL)+4Optimal Observation-Intervention Trade-Off in Optimisation Problems with Causal Structure
We consider the problem of optimising an expensive-to-evaluate grey-box objective function, within a finite budget, where known side-information exists in the form of the causal structure between the design variables. St…
Bayesian Optimisation