mapXy.gr.sigmoid

This function implements the gradient of the sigmoid map function with
respect to coeff used for logistic regression in the form required by
<code>EmpiricalRiskMinimizationDP.CMS</code>.

internal

An implementation of common statistical analysis and models with
differential privacy (Dwork et al., 2006a) <doi:10.1007/11681878_14>
guarantees. The package contains, for example, functions providing
differentially private computations of mean, variance, median, histograms,
and contingency tables. It also implements some statistical models and
machine learning algorithms such as linear regression (Kifer et al., 2012)
<https://proceedings.mlr.press/v23/kifer12.html>
and SVM (Chaudhuri et al., 2011)
<https://jmlr.org/papers/v12/chaudhuri11a.html>. In addition, it implements
some popular randomization mechanisms, including
the Laplace mechanism (Dwork et al., 2006a)
<doi:10.1007/11681878_14>, Gaussian mechanism (Dwork et al., 2006b)
<doi:10.1007/11761679_29>, analytic Gaussian mechanism (Balle & Wang, 2018)
<https://proceedings.mlr.press/v80/balle18a.html>, and exponential mechanism
(McSherry & Talwar, 2007) <doi:10.1109/FOCS.2007.66>.

Spencer Giddens

DPpack

Differentially Private Statistical Analysis and Machine Learning

Fang Liu

mapXy.gr.sigmoid function

<dl><dt>X</dt>
<dd>Matrix of data.</dd>
<dt>coeff</dt>
<dd>Vector or matrix of coefficients or weights.</dd></dl>

Arguments

Sigmoid Map Function Gradient — mapXy.gr.sigmoid

<dl>

<dt>X</dt>
<dd>Matrix of data.</dd>


<dt>coeff</dt>
<dd>Vector or matrix of coefficients or weights.</dd>

</dl>

mapXy.gr.sigmoid: Sigmoid Map Function Gradient

Description

Usage

Value

Arguments

Examples