Multitask Spectral Learning of Weighted Automata
Guillaume Rabusseau, Borja Balle, Joelle Pineau
Abstract
We consider the problem of estimating multiple related functions computed by weighted automata~(WFA). We first present a natural notion of relatedness between WFAs by considering to which extent several WFAs can share a common underlying representation. We then introduce the model of vector-valued WFA which conveniently helps us formalize this notion of relatedness. Finally, we propose a spectral learning algorithm for vector-valued WFAs to tackle the multitask learning problem. By jointly learning multiple tasks in the form of a vector-valued WFA, our algorithm enforces the discovery of a representation space shared between tasks. The benefits of the proposed multitask approach are theoretically motivated and showcased through experiments on both synthetic and real world datasets.
BibTeX
@inproceedings{NIPS2017_e655c771,
author = {Rabusseau, Guillaume and Balle, Borja and Pineau, Joelle},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Multitask Spectral Learning of Weighted Automata},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/e655c7716a4b3ea67f48c6322fc42ed6-Paper.pdf},
volume = {30},
year = {2017}
}