sMTL
Sparse Multi-Task Learning
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) doi:10.48550/arXiv.2212.08697.
- Version0.1.0
- R versionunknown
- LicenseMIT
- LicenseLICENSE
- Needs compilation?No
- Last release02/06/2023
Team
Gabriel Loewinger
Rahul Mazumder
Show author detailsRolesAuthorKayhan Behdin
Show author detailsRolesAuthorGiovanni Parmigiani
Show author detailsRolesAuthorNational Science Foundation Grant DMS1810829
Show author detailsRolesfndNational Science Foundation Grant DMS2113707
Show author detailsRolesfndNational Science Foundation Grant NSF-IIS1718258
Show author detailsRolesfndOffice of Naval Research Grant ONR N000142112841
Show author detailsRolesfndNational Institute on Drug Abuse (NIH) Grant F31DA052153
Show author detailsRolesfnd
Insights
Last 30 days
This package has been downloaded 142 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 4 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 1,881 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Jan 21, 2025 with 27 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports5 packages
- Suggests2 packages