Papers Bayesian Optimisation
“Bayesian Optimisation” 태그가 달린 논문 221편 · 필터 해제
Will More Expressive Graph Neural Networks do Better on Generative Tasks?
Graph generation poses a significant challenge as it involves predicting a complete graph with multiple nodes and edges based on simply a given label. This task also carries fundamental importance to numerous real-world …
Bayesian OptimisationGraph GenerationGraph Neural NetworkMolecular Graph GenerationMachine Learning-Assisted Discovery of Flow Reactor Designs
Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge f…
Bayesian OptimisationAdaptive Batch Sizes for Active Learning A Probabilistic Numerics Approach
Active learning parallelization is widely used, but typically relies on fixing the batch size throughout experimentation. This fixed approach is inefficient because of a dynamic trade-off between cost and speed -- larger…
Active LearningBayesian OptimisationBayesian OptimizationDrug DiscoveryBayesian 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. Traditional graph search algorithms can be applied in this case, but they may be sam…
Bayesian OptimisationBayesian Optimisation Against Climate Change: Applications and Benchmarks
Bayesian optimisation is a powerful method for optimising black-box functions, popular in settings where the true function is expensive to evaluate and no gradient information is available. Bayesian optimisation can impr…
Bayesian OptimisationLearning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning
Online tuning of real-world plants is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learnin…
Bayesian OptimisationOpen-Ended Question AnsweringEnd-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
Meta-Bayesian optimisation (meta-BO) aims to improve the sample efficiency of Bayesian optimisation by leveraging data from related tasks. While previous methods successfully meta-learn either a surrogate model or an acq…
Bayesian OptimisationInductive BiasReinforcement Learning (RL)validMulti-objective optimisation via the R2 utilities
The goal of multi-objective optimisation is to identify a collection of points which describe the best possible trade-offs between the multiple objectives. In order to solve this vector-valued optimisation problem, pract…
Bayesian OptimisationBayesian OptimizationNUBO: A Transparent Python Package for Bayesian Optimization
NUBO, short for Newcastle University Bayesian Optimization, is a Bayesian optimization framework for optimizing expensive-to-evaluate black-box functions, such as physical experiments and computer simulators. Bayesian op…
Bayesian OptimisationBayesian OptimizationGaussian ProcessesUncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI
Modern AI practices all strive towards the same goal: better results. In the context of deep learning, the term "results" often refers to the achieved accuracy on a competitive problem set. In this paper, we adopt an ide…
Bayesian OptimisationDeep LearningApplications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes
In many areas of the observational and experimental sciences data is scarce. Data observation in high-energy astrophysics is disrupted by celestial occlusions and limited telescope time while data derived from laboratory…
Bayesian OptimisationGaussian Processesscientific discoveryProtein Sequence Design with Batch Bayesian Optimisation
Protein sequence design is a challenging problem in protein engineering, which aims to discover novel proteins with useful biological functions. Directed evolution is a widely-used approach for protein sequence design, w…
Bayesian OptimisationBayesian OptimizationProtein DesignAutomated 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. This limits the utility of sequential 1D scanning of experimental parameters for t…
Bayesian OptimisationMONGOOSE: 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. A primary contributor to the cost of evaluating such black-box objective functions is often…
Bayesian OptimisationMeta-LearningDetection and classification of vocal productions in large scale audio recordings
We propose an automatic data processing pipeline to extract vocal productions from large-scale natural audio recordings and classify these vocal productions. The pipeline is based on a deep neural network and adresses bo…
Bayesian OptimisationData AugmentationTransfer LearningDelayed Feedback in Kernel Bandits
Black box optimisation of an unknown function from expensive and noisy evaluations is a ubiquitous problem in machine learning, academic research and industrial production. An abstraction of the problem can be formulated…
Bayesian OptimisationRecommendation SystemsAre Random Decompositions all we need in High Dimensional Bayesian Optimisation?
Learning decompositions of expensive-to-evaluate black-box functions promises to scale Bayesian optimisation (BO) to high-dimensional problems. However, the success of these techniques depends on finding proper decomposi…
AllBayesian OptimisationIntrinsic Bayesian Optimisation on Complex Constrained Domain
Motivated by the success of Bayesian optimisation algorithms in the Euclidean space, we propose a novel approach to construct Intrinsic Bayesian optimisation (In-BO) on manifolds with a primary focus on complex constrain…
Bayesian OptimisationGaussian ProcessesContextual Causal Bayesian Optimisation
Causal Bayesian optimisation (CaBO) combines causality with Bayesian optimisation (BO) and shows that there are situations where the optimal reward is not achievable if causal knowledge is ignored. While CaBO exploits ca…
Bayesian OptimisationMulti-Armed BanditsAutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning
Large pretrained language models are widely used in downstream NLP tasks via task-specific fine-tuning, but such procedures can be costly. Recently, Parameter-Efficient Fine-Tuning (PEFT) methods have achieved strong tas…
Bayesian OptimisationNeural Architecture Searchparameter-efficient fine-tuning