Bayesian Optimization
518 papers with code • 0 benchmarks • 1 datasets
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Libraries
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Most implemented papers
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
kubeflow/katib
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Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters.
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions.
Auto-Keras: An Efficient Neural Architecture Search System
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
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
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
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
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
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Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations.
Max-value Entropy Search for Efficient Bayesian Optimization
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
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.
