Multitask spectral learning of weighted automata
Advances in neural information processing systems, 2017•proceedings.neurips.cc
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 …
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 …
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.
proceedings.neurips.cc
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