tuneZscale

tuneZscale: scale lambda_z depending on magnitude. For internal package use only.

Implements L0-constrained Multi-Task Learning and domain generalization algorithms. The algorithms are coded in Julia allowing for fast implementations of the coordinate descent and local combinatorial search algorithms. For more details, see a preprint of the paper: Loewinger et al., (2022) <arXiv:2212.08697>.

Gabriel Loewinger

sMTL

Sparse Multi-Task Learning

Kayhan Behdin

Giovanni Parmigiani

Rahul Mazumder

National Science Foundation Grant DMS1810829

National Science Foundation Grant DMS2113707

National Science Foundation Grant NSF-IIS1718258,

Office of Naval Research Grant ONR N000142112841

National Institute on Drug Abuse (NIH) Grant F31DA052153

tuneZscale function

<dl><dt>tune.grid</dt>
<dd>A dataframe</dd>
<dt>rhoScale</dt>
<dd>A dataframe</dd></dl>

Arguments

tuneZscale: scale lambda_z depending on magnitude. For internal package use only. — tuneZscale

<dl>

<dt>tune.grid</dt>
<dd>A dataframe</dd>


<dt>rhoScale</dt>
<dd>A dataframe</dd>

</dl>

tuneZscale: scale lambda_z depending on magnitude. For internal package use only.

tuneZscale: tuneZscale: scale lambda_z depending on magnitude. For internal package use only.

Description

Usage

Value

Arguments