Papers Bayesian Optimisation
“Bayesian Optimisation” 태그가 달린 논문 221편 · 필터 해제
Modelling the Effects of Hearing Loss on Neural Coding in the Auditory Midbrain with Variational Conditioning
The mapping from sound to neural activity that underlies hearing is highly non-linear. The first few stages of this mapping in the cochlea have been modelled successfully, with biophysical models built by hand and, more …
Bayesian OptimisationSoft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration
Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and inefficient search. We propose Soft Reasoning, an embedding-based search framework that optimises the embedding of the first token…
Bayesian OptimisationDiversityHeuristic SearchMachine Learning-based Regional Cooling Demand Prediction with Optimised Dataset Partitioning
In the context of global warming, even relatively cooler countries like the UK are experiencing a rise in cooling demand, particularly in southern regions such as London. This growing demand, especially during the summer…
Bayesian Optimisationenergy managementFew-shot crack image classification using clip based on bayesian optimization
This study proposes a novel few-shot crack image classification model based on CLIP and Bayesian optimization. By combining multimodal information and Bayesian approach, the model achieves efficient classification of cra…
Bayesian OptimisationBayesian OptimizationClassificationimage-classification+1Nested Expectations with Kernel Quadrature
This paper considers the challenging computational task of estimating nested expectations. Existing algorithms, such as nested Monte Carlo or multilevel Monte Carlo, are known to be consistent but require a large number …
Bayesian OptimisationMean-Field Bayesian Optimisation
We address the problem of optimising the average payoff for a large number of cooperating agents, where the payoff function is unknown and treated as a black box. While standard Bayesian Optimisation (BO) methods struggl…
Bayesian OptimisationMaximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning
As we transition from Narrow Artificial Intelligence towards Artificial Super Intelligence, users are increasingly concerned about their privacy and the trustworthiness of machine learning (ML) technology. A common denom…
Bayesian OptimisationFederated LearningModel PoisoningPrivacy PreservingMulti-view Bayesian optimisation in reduced dimension for engineering design
Bayesian optimisation is an adaptive sampling strategy for constructing a Gaussian process surrogate to emulate a black-box computational model with the aim of efficiently searching for the global minimum. However, Gauss…
Bayesian OptimisationGaussian ProcessesMULTI-VIEW LEARNINGDimensionality Reduction Techniques for Global Bayesian Optimisation
Bayesian Optimisation (BO) is a state-of-the-art global optimisation technique for black-box problems where derivative information is unavailable, and sample efficiency is crucial. However, improving the general scalabil…
Bayesian OptimisationDimensionality ReductionGaussian Processesglobal-optimization+1Nonmyopic Global Optimisation via Approximate Dynamic Programming
Unconstrained global optimisation aims to optimise expensive-to-evaluate black-box functions without gradient information. Bayesian optimisation, one of the most well-known techniques, typically employs Gaussian processe…
Bayesian OptimisationGaussian ProcessesSequential Decision MakingGraph Agnostic Causal Bayesian Optimisation
We study the problem of globally optimising a target variable of an unknown causal graph on which a sequence of soft or hard interventions can be performed. The problem of optimising the target variable associated with a…
Bayesian OptimisationBayesian OptimizationLarge Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level
We introduce Agent K v1.0, an end-to-end autonomous data science agent designed to automate, optimise, and generalise across diverse data science tasks. Fully automated, Agent K v1.0 manages the entire data science life …
Bayesian OptimisationBenchmarkingFeature EngineeringResidual Deep Gaussian Processes on Manifolds
We propose practical deep Gaussian process models on Riemannian manifolds, similar in spirit to residual neural networks. With manifold-to-manifold hidden layers and an arbitrary last layer, they can model manifold- and …
Bayesian OptimisationGaussian ProcessesSample-efficient Bayesian Optimisation Using Known Invariances
Bayesian optimisation (BO) is a powerful framework for global optimisation of costly functions, using predictions from Gaussian process models (GPs). In this work, we apply BO to functions that exhibit invariance to a kn…
Bayesian OptimisationSpectral Representations for Accurate Causal Uncertainty Quantification with Gaussian Processes
Accurate uncertainty quantification for causal effects is essential for robust decision making in complex systems, but remains challenging in non-parametric settings. One promising framework represents conditional distri…
Bayesian OptimisationDecision MakingGaussian ProcessesUncertainty QuantificationPrincipled Bayesian Optimisation in Collaboration with Human Experts
Bayesian optimisation for real-world problems is often performed interactively with human experts, and integrating their domain knowledge is key to accelerate the optimisation process. We consider a setup where experts p…
Bayesian OptimisationBayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
Bayesian Optimization (BO) is widely used for optimising black-box functions but requires us to specify the length scale hyperparameter, which defines the smoothness of the functions the optimizer will consider. Most cur…
Bayesian OptimisationBayesian OptimizationLarge Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language
Autonomous tuning of particle accelerators is an active and challenging field of research with the goal of enabling novel accelerator technologies cutting-edge high-impact applications, such as physics discovery, cancer …
Bayesian OptimisationUnsupervised machine learning for data-driven rock mass classification: addressing limitations in existing systems using drilling data
Rock mass classification systems are crucial for assessing stability and risk in underground construction globally and guiding support and excavation design. However, these systems, developed primarily in the 1970s, lack…
Bayesian OptimisationClusteringRepresentation LearningA Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting
Parallelisation in Bayesian optimisation is a common strategy but faces several challenges: the need for flexibility in acquisition functions and kernel choices, flexibility dealing with discrete and continuous variables…
Bayesian OptimisationBayesian Optimization