@inproceedings{zwick_symreg-mt:_2014,
	address = {Nancy, France},
	title = {{SymReg}-{MT}: Iterative Multi-task Feature Learning Through Weighted Symbolic Regression},
	abstract = {We present a novel approach to unify regression models learned in parallel on different but related datasets using multi-task feature learning based on symbolic regression. The {FFX} framework (Fast Function Extraction) is used for symbolic regression. It relies on regularized linear regression instead of genetic programming, thus providing a scalable and deterministic framework for implementation. {FFX} provides a basis for an iterative multi-task feature learning approach. Models learned on separate tasks are coupled by iteratively promoting common terms and penalizing seldom occurring terms, leading to improved consistency and interpretability of models across tasks and improved stability on new data. We conducted experiments on both real world datasets and synthetic datasets. The results show that already the use of this basic approach leads to models which share more features, show less complexity and still retain the same model performance when compared to a single symbolic regression run. Finally, we provide ideas and plans for future improvements based on our first implementation.},
	author = {Zwick, Michael and Schöner, Holger and Rezaie, Ehsan},
	booktitle = {First International Workshop on Learning over Multiple Contexts (LMCE) @ ECML},
	month = sep,
	year = {2014},
	keywords = {ecml, fast function extraction, ffx, multitask learning, symbolic regression, transfer learning},
	file = {download/publications/Zwick2014_LMCEatECML_SymRegMT.pdf}
}