Papers Bayesian Optimisation
“Bayesian Optimisation” 태그가 달린 논문 221편 · 필터 해제
Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Bayesian Optimisation (BO) refers to a class of methods for global optimisation of a function $f$ which is only accessible via point evaluations. It is typically used in settings where $f$ is expensive to evaluate. A com…
Bayesian OptimisationBIG-bench Machine LearningModel SelectionNeural Architecture SearchProcess-constrained batch Bayesian optimisation
Abstract Prevailing batch Bayesian optimisation methods allow all control variables to be freely altered at each iteration. Real-world experiments, however, often have physical limitations making it time-consuming to alt…
Bayesian OptimisationGPflowOpt: A Bayesian Optimization Library using TensorFlow
A novel Python framework for Bayesian optimization known as GPflowOpt is introduced. The package is based on the popular GPflow library for Gaussian processes, leveraging the benefits of TensorFlow including automatic di…
Bayesian OptimisationBayesian OptimizationGaussian ProcessesGPUFast Information-theoretic Bayesian Optimisation
Information-theoretic Bayesian optimisation techniques have demonstrated state-of-the-art performance in tackling important global optimisation problems. However, current information-theoretic approaches require many app…
Bayesian OptimisationBayesian Optimisation for Safe Navigation under Localisation Uncertainty
In outdoor environments, mobile robots are required to navigate through terrain with varying characteristics, some of which might significantly affect the integrity of the platform. Ideally, the robot should be able to i…
Bayesian OptimisationNavigateGeneralising Random Forest Parameter Optimisation to Include Stability and Cost
Random forests are among the most popular classification and regression methods used in industrial applications. To be effective, the parameters of random forests must be carefully tuned. This is usually done by choosing…
Bayesian OptimisationAsynchronous Parallel Bayesian Optimisation via Thompson Sampling
We design and analyse variations of the classical Thompson sampling (TS) procedure for Bayesian optimisation (BO) in settings where function evaluations are expensive, but can be performed in parallel. Our theoretical an…
Bayesian OptimisationThompson SamplingDifferential Evolution and Bayesian Optimisation for Hyper-Parameter Selection in Mixed-Signal Neuromorphic Circuits Applied to UAV Obstacle Avoidance
The Lobula Giant Movement Detector (LGMD) is a an identified neuron of the locust that detects looming objects and triggers its escape responses. Understanding the neural principles and networks that lead to these fast a…
Bayesian OptimisationEfficient acquisition rules for model-based approximate Bayesian computation
Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible. However, many ABC algorithms require a large number of simulations, …
Bayesian InferenceBayesian OptimisationGaussian ProcessesMulti-fidelity Bayesian Optimisation with Continuous Approximations
Bandit methods for black-box optimisation, such as Bayesian optimisation, are used in a variety of applications including hyper-parameter tuning and experiment design. Recently, \emph{multi-fidelity} methods have garnere…
Bayesian OptimisationMax-value Entropy Search for Efficient Bayesian Optimization
Entropy Search (ES) and Predictive Entropy Search (PES) are popular and empirically successful Bayesian Optimization techniques. Both rely on a compelling information-theoretic motivation, and maximize the information ga…
Bayesian OptimisationBayesian OptimizationAlternating Optimisation and Quadrature for Robust Control
Bayesian optimisation has been successfully applied to a variety of reinforcement learning problems. However, the traditional approach for learning optimal policies in simulators does not utilise the opportunity to impro…
Bayesian OptimisationReinforcement LearningGLASSES: Relieving The Myopia Of Bayesian Optimisation
We present GLASSES: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search. The majority of global optimisation approaches in use are myopic, in only considering the impact of the next…
Bayesian OptimisationBayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
We consider the problem of approximate Bayesian parameter inference in non-linear state-space models with intractable likelihoods. Sequential Monte Carlo with approximate Bayesian computations (SMC-ABC) is one approach t…
Bayesian OptimisationState Space ModelsBatch Bayesian Optimization via Local Penalization
The popularity of Bayesian optimization methods for efficient exploration of parameter spaces has lead to a series of papers applying Gaussian processes as surrogates in the optimization of functions. However, most propo…
Bayesian OptimisationBayesian OptimizationEfficient ExplorationGaussian ProcessesBayesian Policy Reuse
A long-lived autonomous agent should be able to respond online to novel instances of tasks from a familiar domain. Acting online requires 'fast' responses, in terms of rapid convergence, especially when the task instance…
Bayesian OptimisationHigh Dimensional Bayesian Optimisation and Bandits via Additive Models
Bayesian Optimisation (BO) is a technique used in optimising a $D$-dimensional function which is typically expensive to evaluate. While there have been many successes for BO in low dimensions, scaling it to high dimensio…
Additive modelsBayesian OptimisationFace DetectionVocal Bursts Intensity PredictionBayesian Optimisation for Machine Translation
This paper presents novel Bayesian optimisation algorithms for minimum error rate training of statistical machine translation systems. We explore two classes of algorithms for efficiently exploring the translation space,…
Bayesian OptimisationMachine TranslationTranslationHeteroscedastic Treed Bayesian Optimisation
Optimising black-box functions is important in many disciplines, such as tuning machine learning models, robotics, finance and mining exploration. Bayesian optimisation is a state-of-the-art technique for the global opti…
Bayesian OptimisationBIG-bench Machine LearningAutomated Machine Learning on Big Data using Stochastic Algorithm Tuning
We introduce a means of automating machine learning (ML) for big data tasks, by performing scalable stochastic Bayesian optimisation of ML algorithm parameters and hyper-parameters. More often than not, the critical tuni…
Bayesian OptimisationBenchmarkingBIG-bench Machine LearningTime Series+2