multiTaskRmse_MT

multiTaskRmse: calculate average (across tasks) RMSE for multi-label prediction problems

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

multiTaskRmse_MT function

<dl><dt>data</dt>
<dd>A matrix including outcome vector/matrix and design matrix to test RMSE on</dd>
<dt>K</dt>
<dd>An integer of number of studies/tasks</dd>
<dt>beta</dt>
<dd>A matrix of estimated beta coefficients where each task is in a different column</dd></dl>

Arguments

multiTaskRmse: calculate average (across tasks) RMSE for multi-label prediction problems — multiTaskRmse_MT

<dl>

<dt>data</dt>
<dd>A matrix including outcome vector/matrix and design matrix to test RMSE on</dd>


<dt>K</dt>
<dd>An integer of number of studies/tasks</dd>


<dt>beta</dt>
<dd>A matrix of estimated beta coefficients where each task is in a different column</dd>

</dl>

multiTaskRmse: calculate average (across tasks) RMSE for multi-label prediction problems

multiTaskRmse_MT: multiTaskRmse: calculate average (across tasks) RMSE for multi-label prediction problems

Description

Usage

Value

Arguments