Cross-Domain Multitask Learning with Latent Probit Models
This addresses multitask learning challenges in cross-domain settings, but appears incremental as it builds on existing latent and probit model frameworks.
The paper tackles the problem of learning multiple tasks across heterogeneous domains by proposing a latent probit model that jointly learns domain transforms and a shared classifier, with theoretical error bounds and empirical validation on real datasets.
Learning multiple tasks across heterogeneous domains is a challenging problem since the feature space may not be the same for different tasks. We assume the data in multiple tasks are generated from a latent common domain via sparse domain transforms and propose a latent probit model (LPM) to jointly learn the domain transforms, and the shared probit classifier in the common domain. To learn meaningful task relatedness and avoid over-fitting in classification, we introduce sparsity in the domain transforms matrices, as well as in the common classifier. We derive theoretical bounds for the estimation error of the classifier in terms of the sparsity of domain transforms. An expectation-maximization algorithm is derived for learning the LPM. The effectiveness of the approach is demonstrated on several real datasets.