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What is a "surrogate model"?

What is Bayesian optimization? Introduction Bayesian optimization (BO) is an optimization technique used to model an unknown (usually continuous) function $f: \mathbb{R}^d \rightarrow Y$, where ...
nbro's user avatar
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5 votes

What is a "surrogate model"?

A surrogate model is a simplified model. It is a mapping $y_S=f_S(x)$ that approximates the original model $y=f(x)$, in a given domain, reasonably well. Source: Engineering Design via Surrogate ...
Javier-Acuna's user avatar
2 votes
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How can I interpret the value returned by score(X) method of sklearn.neighbors.KernelDensity?

The KernelDensity model learns a probability distribution from the training data. The score reflects how likely it is that any given sample has been drawn from the ...
Chillston's user avatar
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2 votes

Bayesian hyperparameter optimization, is it worth it?

Efficiently integrating HPO frameworks into an existing project is non-trivial. Most common datasets/tasks already have established architectures/hyperparameters/etc. and require only a few additional ...
rhdxor's user avatar
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1 vote
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In which community does using a Bayesian regression model as a reward function with exploration vs. exploitation challenges fall under?

One community that has very recently been attacking problems of the type posed by your question is the Bayesian sequential optimal experimental design (Bayesian sOED) community. The Bayesian sOED ...
DeepQZero's user avatar
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1 vote
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Are there Python packages for recent Bayesian optimization methods?

Apart from the Scikit-Optimize package related to Scikit-Learn, following are some of the packages related to Bayesian optimization: GPyOpt pyGPGO Hyperopt bayesian-optimization safeopt RoBO
programmer's user avatar

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