In this work, we introduce a novel approach to achieve transfer learning across different datasets as well as different objectives.
Sep 30, 2019 · The main idea is to regress the mapping from hyperparameter to objective quantiles with a semi-parametric Gaussian Copula distribution, which ...
In this work, we introduce a novel approach to achieve transfer learning across different datasets as well as dif- ferent objectives. The main idea is to ...
The main idea is to reparametrize raw metrics as quantiles via the probability integral transform, and learn a mapping from hyperparameters to metric quantiles.
The main idea is to regress the mapping from hyperparameter to objective quantiles with a semi-parametric Gaussian Copula distribution, which provides ...
Bayesian optimization (BO) is a popular methodology to tune the hyperparameters of expensive black-box functions. Despite its success, standard BO focuses ...
Abstract. Bayesian optimization (BO) is a popular method- ology to tune the hyperparameters of expensive black-box functions. Traditionally, BO focuses.
This work introduces a novel approach to achieve transfer learning across different datasets as well as different objectives, to regress the mapping from ...
This code reproduces the method of the paper A quantile approach for hyperparameter transfer learning published at ICML 2020.
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Jul 12, 2020 · The main idea is to regress the mapping from hyperparameter to objective quantiles with a semi-parametric Gaussian Copula distribution, which ...