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# Bayesian Optimisation Edit

21 papers with code · Methodology

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# BoTorch: Programmable Bayesian Optimization in PyTorch

14 Oct 2019pytorch/botorch

Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, molecular chemistry, and experimental design.

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# Batch Bayesian Optimization via Local Penalization

29 May 2015SheffieldML/GPyOpt

The approach assumes that the function of interest, $f$, is a Lipschitz continuous function.

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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.

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# GPflowOpt: A Bayesian Optimization Library using TensorFlow

10 Nov 2017GPflow/GPflowOpt

A novel Python framework for Bayesian optimization known as GPflowOpt is introduced.

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# Neural Architecture Search with Bayesian Optimisation and Optimal Transport

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.

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# Effective Estimation of Deep Generative Language Models

17 Apr 2019tom-pelsmaeker/deep-generative-lm

We concentrate on one such model, the variational auto-encoder, which we argue is an important building block in hierarchical probabilistic models of language.

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# Fast Information-theoretic Bayesian Optimisation

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

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Black-box adversarial attacks require a large number of attempts before finding successful adversarial examples that are visually indistinguishable from the original input.

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# 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.

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# 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.

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