Bayesian Optimization

496 papers with code • 0 benchmarks • 1 datasets

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Use these libraries to find Bayesian Optimization models and implementations
3 papers
3 papers
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Most implemented papers

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization

kubeflow/katib 21 Mar 2016

Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters.

Auto-Keras: An Efficient Neural Architecture Search System

keras-team/autokeras 27 Jun 2018

In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search.

The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development

HDI-Project/BTB 22 May 2019

To address these problems, we introduce the Machine Learning Bazaar, a new framework for developing machine learning and automated machine learning software systems.

A Tutorial on Bayesian Optimization

wujian16/Cornell-MOE 8 Jul 2018

It builds a surrogate for the objective and quantifies the uncertainty in that surrogate using a Bayesian machine learning technique, Gaussian process regression, and then uses an acquisition function defined from this surrogate to decide where to sample.

MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements

thomas-young-2013/lite-bo 5 Dec 2020

Instead of sampling configurations randomly in HB, BOHB samples configurations based on a BO surrogate model, which is constructed with the high-fidelity measurements only.

Practical Bayesian Optimization of Machine Learning Algorithms

HIPS/Spearmint NeurIPS 2012

In this work, we consider the automatic tuning problem within the framework of Bayesian optimization, in which a learning algorithm's generalization performance is modeled as a sample from a Gaussian process (GP).

Scalable Bayesian Optimization Using Deep Neural Networks

automl/pybnn 19 Feb 2015

Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations.

Max-value Entropy Search for Efficient Bayesian Optimization

zi-w/Max-value-Entropy-Search ICML 2017

We propose a new criterion, Max-value Entropy Search (MES), that instead uses the information about the maximum function value.

mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions

mlr-org/mlrMBO 9 Mar 2017

We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the given objective function through a surrogate regression model.