Papers Bayesian Optimisation
“Bayesian Optimisation” 태그가 달린 논문 221편 · 필터 해제
MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimation
Optimisation problems often have multiple conflicting objectives that can be computationally and/or financially expensive. Mono-surrogate Bayesian optimisation (BO) is a popular model-based approach for optimising such b…
Bayesian OptimisationDensity Ratio EstimationWind Farm Layout Optimisation using Set Based Multi-objective Bayesian Optimisation
Wind energy is one of the cleanest renewable electricity sources and can help in addressing the challenge of climate change. One of the drawbacks of wind-generated energy is the large space necessary to install a wind fa…
Bayesian OptimisationAdaptive Model Predictive Control by Learning Classifiers
Stochastic model predictive control has been a successful and robust control framework for many robotics tasks where the system dynamics model is slightly inaccurate or in the presence of environment disturbances. Despit…
Bayesian OptimisationDensity Ratio EstimationmodelModel Predictive ControlBayesian Optimisation-Assisted Neural Network Training Technique for Radio Localisation
Radio signal-based (indoor) localisation technique is important for IoT applications such as smart factory and warehouse. Through machine learning, especially neural networks methods, more accurate mapping from signal fe…
Bayesian OptimisationBayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty
We propose an adaptive optimisation approach for tuning stochastic model predictive control (MPC) hyper-parameters while jointly estimating probability distributions of the transition model parameters based on performanc…
Bayesian OptimisationmodelModel Predictive ControlBayesian Optimisation for Active Monitoring of Air Pollution
Air pollution is one of the leading causes of mortality globally, resulting in millions of deaths each year. Efficient monitoring is important to measure exposure and enforce legal limits. New low-cost sensors can be dep…
Bayesian OptimisationUncertainty Aware System Identification with Universal Policies
Sim2real transfer is primarily concerned with transferring policies trained in simulation to potentially noisy real world environments. A common problem associated with sim2real transfer is estimating the real-world envi…
Bayesian Optimisationcontinuous-controlContinuous ControlFast Model-based Policy Search for Universal Policy Networks
Adapting an agent's behaviour to new environments has been one of the primary focus areas of physics based reinforcement learning. Although recent approaches such as universal policy networks partially address this issue…
Bayesian OptimisationBayesian Optimisation for Mixed-Variable Inputs using Value Proposals
Many real-world optimisation problems are defined over both categorical and continuous variables, yet efficient optimisation methods such asBayesian Optimisation (BO) are not designed tohandle such mixed-variable search …
Bayesian OptimisationAdjoint-aided inference of Gaussian process driven differential equations
Linear systems occur throughout engineering and the sciences, most notably as differential equations. In many cases the forcing function for the system is unknown, and interest lies in using noisy observations of the sys…
Bayesian InferenceBayesian OptimisationOMLT: Optimization & Machine Learning Toolkit
The optimization and machine learning toolkit (OMLT) is an open-source software package incorporating neural network and gradient-boosted tree surrogate models, which have been trained using machine learning, into larger…
Bayesian OptimisationBIG-bench Machine LearningDecision MakingAntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation
Antibodies are canonically Y-shaped multimeric proteins capable of highly specific molecular recognition. The CDRH3 region located at the tip of variable chains of an antibody dominates antigen-binding specificity. There…
Bayesian OptimisationBenchmarkingSpecificityBayesian Deep Learning for Interactive Community Question Answering
Human-in-the-loop interactive learning has been shown to be effective for best solution selection tasks. Bayesian Optimisation (BO) reduces the amount of user interaction required but has so far relied on shallow models …
Bayesian OptimisationCommunity Question AnsweringDeep LearningQuestion AnsweringTowards automated optimisation of residual convolutional neural networks for electrocardiogram classification
The interpretation of the electrocardiogram (ECG) gives clinical information and helps in assessing heart function. There are distinct ECG patterns associated with a specific class of arrythmia. The convolutional neural …
Bayesian OptimisationEvolutionary AlgorithmsKernel Functional Optimisation
Traditional methods for kernel selection rely on parametric kernel functions or a combination thereof and although the kernel hyperparameters are tuned, these methods often provide sub-optimal results due to the limitati…
Bayesian OptimisationAdversarial Attacks on Graph Classifiers via Bayesian Optimisation
Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial attacks. While the majority of the literature focuses on such vuln…
Adversarial RobustnessBayesian OptimisationClassificationGraph ClassificationBOiLS: Bayesian Optimisation for Logic Synthesis
Optimising the quality-of-results (QoR) of circuits during logic synthesis is a formidable challenge necessitating the exploration of exponentially sized search spaces. While expert-designed operations aid in uncovering …
Bayesian OptimisationNavigateSparse Adversarial Video Attacks with Spatial Transformations
In recent years, a significant amount of research efforts concentrated on adversarial attacks on images, while adversarial video attacks have seldom been explored. We propose an adversarial attack strategy on videos, cal…
Adversarial AttackBayesian OptimisationSSIMApproximate Neural Architecture Search via Operation Distribution Learning
The standard paradigm in Neural Architecture Search (NAS) is to search for a fully deterministic architecture with specific operations and connections. In this work, we instead propose to search for the optimal operation…
Bayesian OptimisationNeural Architecture SearchAdversarial Attacks on Graph Classification via Bayesian Optimisation
Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial attacks. While the majority of the literature focuses on such vuln…
Adversarial RobustnessBayesian OptimisationClassificationGraph Classification