Publication date
July 2019
DOIAbstract
Added a new method GpRegressor.gradient, which allows for the calculation of the mean and variance of the gradient of the regression estimate. This is useful for robust estimation of derivatives in noisy data
Added a new method GpRegressor.gradient, which allows for the calculation of the mean and variance of the gradient of the regression estimate. This is useful for robust estimation of derivatives in noisy data
International audienceThe generalized likelihood ratio (GLR) method is a recently introduced gradien...
We analyse the properties of an unbiased gradient estimator of the evidence lower bound (ELBO) for ...
In many situations it is of primary interest to estimate the rate of change of the relationship betw...
Rather than assuming the mean of the Gaussian process is zero, GpRegressor now treats the mean as a ...
This release contains significant improvements to the GpRegressor class, including: A new option to...
Improved the efficiency of linear algebra calculations in GpRegressor related to hyper-parameter opt...
Added a WhiteNoise covariance function to model the presence of Gaussian noise on input data for Gau...
The reparameterization gradient has become a widely used method to obtain Monte Carlo gradients to o...
© 2012 Schiess et al.; licensee Springer. This is an Open Access article distributed under the terms...
Gradient estimation -- approximating the gradient of an expectation with respect to the parameters o...
Inference in mixed models is often based on the marginal distribution obtained from integrating out ...
Optimization with noisy gradients has become ubiquitous in statistics and machine learning. Reparame...
In regression problems over ℝd, the unknown function f often varies more in some coordinates than in...
We show how single-run-based measure-valued differentiation gradient estimators can be obtained. The...
The software contains two components: The HBR code and the gradient calculation code (GCGP) that use...
International audienceThe generalized likelihood ratio (GLR) method is a recently introduced gradien...
We analyse the properties of an unbiased gradient estimator of the evidence lower bound (ELBO) for ...
In many situations it is of primary interest to estimate the rate of change of the relationship betw...
Rather than assuming the mean of the Gaussian process is zero, GpRegressor now treats the mean as a ...
This release contains significant improvements to the GpRegressor class, including: A new option to...
Improved the efficiency of linear algebra calculations in GpRegressor related to hyper-parameter opt...
Added a WhiteNoise covariance function to model the presence of Gaussian noise on input data for Gau...
The reparameterization gradient has become a widely used method to obtain Monte Carlo gradients to o...
© 2012 Schiess et al.; licensee Springer. This is an Open Access article distributed under the terms...
Gradient estimation -- approximating the gradient of an expectation with respect to the parameters o...
Inference in mixed models is often based on the marginal distribution obtained from integrating out ...
Optimization with noisy gradients has become ubiquitous in statistics and machine learning. Reparame...
In regression problems over ℝd, the unknown function f often varies more in some coordinates than in...
We show how single-run-based measure-valued differentiation gradient estimators can be obtained. The...
The software contains two components: The HBR code and the gradient calculation code (GCGP) that use...
International audienceThe generalized likelihood ratio (GLR) method is a recently introduced gradien...
We analyse the properties of an unbiased gradient estimator of the evidence lower bound (ELBO) for ...
In many situations it is of primary interest to estimate the rate of change of the relationship betw...
Add to ORKG