Papers Bayesian Optimisation
“Bayesian Optimisation” 태그가 달린 논문 221편 · 필터 해제
Risk-Averse Bayes-Adaptive Reinforcement Learning
In this work, we address risk-averse Bayes-adaptive reinforcement learning. We pose the problem of optimising the conditional value at risk (CVaR) of the total return in Bayes-adaptive Markov decision processes (MDPs). W…
Bayesian Optimisationreinforcement-learningReinforcement LearningReinforcement Learning (RL)GIBBON: General-purpose Information-Based Bayesian OptimisatioN
This paper describes a general-purpose extension of max-value entropy search, a popular approach for Bayesian Optimisation (BO). A novel approximation is proposed for the information gain -- an information-theoretic quan…
Bayesian OptimisationPoint ProcessesLearning to Explore with Pleasure
Exploration is a long-standing challenge in sequential decision problem in machine learning. This paper investigates the adoption of two theories of optimal stimulation level - "the pacer principle" and the Wundt curve -…
Bayesian OptimisationBIG-bench Machine LearningReinforcement Learning (RL)Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks
The COVID-19 pandemic has affected the world unevenly; while industrial economies have been able to produce the tests necessary to track the spread of the virus and mostly avoided complete lockdowns, developing countries…
Bayesian OptimisationRapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation
Rapid performance recovery from unforeseen environmental perturbations remains a grand challenge in swarm robotics. To solve this challenge, we investigate a behaviour adaptation approach, where one searches an archive o…
Bayesian OptimisationOptimising Placement of Pollution Sensors in Windy Environments
Air pollution is one of the most important causes of mortality in the world. Monitoring air pollution is useful to learn more about the link between health and pollutants, and to identify areas for intervention. Such mon…
Bayesian OptimisationAre we Forgetting about Compositional Optimisers in Bayesian Optimisation?
Bayesian optimisation presents a sample-efficient methodology for global optimisation. Within this framework, a crucial performance-determining subroutine is the maximisation of the acquisition function, a task complicat…
Bayesian OptimisationHEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
In this work we rigorously analyse assumptions inherent to black-box optimisation hyper-parameter tuning tasks. Our results on the Bayesmark benchmark indicate that heteroscedasticity and non-stationarity pose significan…
Bayesian OptimisationBIG-bench Machine LearningHyperparameter OptimizationDiversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations
Many science, engineering, and design optimization problems require balancing the trade-offs between several conflicting objectives. The objectives are often black-box functions whose evaluations are time-consuming and c…
Bayesian OptimisationBayesian OptimizationDiversityAutomatic Clustering for Unsupervised Risk Diagnosis of Vehicle Driving for Smart Road
Early risk diagnosis and driving anomaly detection from vehicle stream are of great benefits in a range of advanced solutions towards Smart Road and crash prevention, although there are intrinsic challenges, especially l…
Anomaly DetectionBayesian OptimisationClusteringPredicting special care during the COVID-19 pandemic: A machine learning approach
More than ever COVID-19 is putting pressure on health systems all around the world, especially in Brazil. In this study we propose an analytical approach based on statistics and machine learning that uses lab exam data c…
Bayesian OptimisationBIG-bench Machine LearningAsynchronous ε-Greedy Bayesian Optimisation
Batch Bayesian optimisation (BO) is a successful technique for the optimisation of expensive black-box functions. Asynchronous BO can reduce wallclock time by starting a new evaluation as soon as another finishes, thus m…
Bayesian OptimisationThompson SamplingMulti-Objective Bayesian Optimisation and Joint Inversion for Active Sensor Fusion
A critical decision process in data acquisition for mineral and energy resource exploration is how to efficiently combine a variety of sensor types and to minimize total cost. We propose a probabilistic framework for mul…
Bayesian OptimisationSensor FusionHeteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control
Model predictive control (MPC) has been successful in applications involving the control of complex physical systems. This class of controllers leverages the information provided by an approximate model of the system's d…
Bayesian Optimisationcontinuous-controlContinuous Controlmodel+1Optimal Use of Multi-spectral Satellite Data with Convolutional Neural Networks
The analysis of satellite imagery will prove a crucial tool in the pursuit of sustainable development. While Convolutional Neural Networks (CNNs) have made large gains in natural image analysis, their application to mult…
Bayesian OptimisationSemantic SegmentationSequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition
We propose an algorithm for Bayesian functional optimisation - that is, finding the function to optimise a process - guided by experimenter beliefs and intuitions regarding the expected characteristics (length-scale, smo…
Bayesian OptimisationBayesian OptimizationSub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
Bayesian optimisation is a popular method for efficient optimisation of expensive black-box functions. Traditionally, BO assumes that the search space is known. However, in many problems, this assumption does not hold. T…
Bayesian OptimisationPreferential Bayesian optimisation with Skew Gaussian Processes
Preferential Bayesian optimisation (PBO) deals with optimisation problems where the objective function can only be accessed via preference judgments, such as "this is better than that" between two candidate solutions (li…
Bayesian OptimisationGaussian ProcessesRecommendation SystemsvalidBayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction
In robotics, methods and softwares usually require optimizations of hyperparameters in order to be efficient for specific tasks, for instance industrial bin-picking from homogeneous heaps of different objects. We present…
Bayesian OptimisationBayesian OptimizationMeta-LearningAutomatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation
Many machine learning models require a training procedure based on running stochastic gradient descent. A key element for the efficiency of those algorithms is the choice of the learning rate schedule. While finding good…
Bayesian OptimisationGaussian ProcessesOpen-Ended Question Answering