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Bayesian Optimisation

9 papers with code · Methodology

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Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly

15 Mar 2019dragonfly/dragonfly

We compare Dragonfly to a suite of other packages and algorithms for global optimisation and demonstrate that when the above methods are integrated, they enable significant improvements in the performance of BO.

BAYESIAN OPTIMISATION

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

NeurIPS 2018 kirthevasank/nasbot

A common use case for BO in machine learning is model selection, where it is not possible to analytically model the generalisation performance of a statistical model, and we resort to noisy and expensive training and validation procedures to choose the best model.

BAYESIAN OPTIMISATION MODEL SELECTION NEURAL ARCHITECTURE SEARCH

Gaussian Process Priors for Dynamic Paired Comparison Modelling

20 Feb 2019martiningram/paired-comparison-gp-laplace

Dynamic paired comparison models, such as Elo and Glicko, are frequently used for sports prediction and ranking players or teams.

BAYESIAN INFERENCE BAYESIAN OPTIMISATION

Fast Information-theoretic Bayesian Optimisation

ICML 2018 rubinxin/FITBO

Information-theoretic Bayesian optimisation techniques have demonstrated state-of-the-art performance in tackling important global optimisation problems.

BAYESIAN OPTIMISATION

Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods

23 Jun 2015compops/gpo-smc-abc

We consider the problem of approximate Bayesian parameter inference in non-linear state-space models with intractable likelihoods.

BAYESIAN OPTIMISATION

Generalising Random Forest Parameter Optimisation to Include Stability and Cost

29 Jun 2017liuchbryan/generalised_forest_tuning

We argue that error reduction is only one of several metrics that must be considered when optimizing random forest parameters for commercial applications.

BAYESIAN OPTIMISATION

Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation

29 Jan 2019a5a/asynchronous-BO

Batch Bayesian optimisation (BO) has been successfully applied to hyperparameter tuning using parallel computing, but it is wasteful of resources: workers that complete jobs ahead of others are left idle.

BAYESIAN OPTIMISATION

Parallel Gaussian process surrogate method to accelerate likelihood-free inference

3 May 2019mjarvenpaa/parallel-GP-SL

We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained.

BAYESIAN INFERENCE BAYESIAN OPTIMISATION

Efficient Bayesian Experimental Design for Implicit Models

23 Oct 2018stevenkleinegesse/bedimplicit

Bayesian experimental design involves the optimal allocation of resources in an experiment, with the aim of optimising cost and performance.

BAYESIAN OPTIMISATION