Sparse Multi-Task Learning
cv.smtl: cross-validation function
grid.gen: generate grid for cross-validation function. For internal pa...
maxEigen: maximum eigenvalue wrapper for Julia TSVD package. internal ...
methods names: give name for printing. Internal package use only.
multiTaskRmse: RMSE for multi-task problems (averaged across tasks)
multiTaskRmse: calculate average (across tasks) RMSE for multi-label p...
predict: predict on smtl model object
reName_cv: rename output from CV. For internal package use only.
rhoScale: scale lambda_z depending on magnitude. For internal package ...
seReturn: find smallest rho within 1 se of smallest cv error. For inte...
smtl: make model-fitting function
smtl_setup: setup Julia path and/or install Julia or Julia packages us...
sparseCV: cross-validation functions. For internal package use only.
sparseCV_MT: internal cross-validation functions. For internal package...
sparseCV_L0: cross-validation functions. For internal package use only...
tuneZscale: scale lambda_z depending on magnitude. For internal packag...
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>.
Useful links