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) <arXiv:2212.08697>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | sMTL_0.1.0.tar.gz |
147.1 KiB |
0.1.0 |
rolling linux/noble R-4.5 | sMTL_0.1.0.tar.gz |
147.0 KiB |
0.1.0 |
rolling source/ R- | sMTL_0.1.0.tar.gz |
65.0 KiB |
0.1.0 |
latest linux/jammy R-4.5 | sMTL_0.1.0.tar.gz |
147.1 KiB |
0.1.0 |
latest linux/noble R-4.5 | sMTL_0.1.0.tar.gz |
147.0 KiB |
0.1.0 |
latest source/ R- | sMTL_0.1.0.tar.gz |
65.0 KiB |
0.1.0 |
2026-04-26 source/ R- | sMTL_0.1.0.tar.gz |
65.0 KiB |
0.1.0 |
2026-04-23 source/ R- | sMTL_0.1.0.tar.gz |
65.0 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | sMTL_0.1.0.zip |
186.2 KiB |
0.1.0 |
2025-04-20 source/ R- | sMTL_0.1.0.tar.gz |
65.0 KiB |