Cross-Domain Multitask Learning with Latent Probit Models

By Shaobo Han, Xuejun Liao, Lawrence Carin. 2012

18 citations recorded by the source corpus.

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 a probit classifier shared 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 parameters. We derive theoretical bounds for the estimation error of the classifier parameters in terms of the sparsity of domain transform matrices. An expectation-maximization algorithm is derived for learning the LPM. The effectiveness of the approach is demonstrated on several real datasets.

Subfields: Artificial Intelligence, Cancer Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Qiang Yang
2. Yu Zhang
3. J. Lai
4. Weishi Zheng
5. Jianfang Hu
6. Jianguo Zhang
7. Eric Eaton
8. Paul Ruvolo

In Cancer Research

1. Yu Zhang
2. Eric Eaton
3. Qiang Yang
4. Weishi Zheng
5. J. Lai

Citations in the corpus

• A Survey on Multi-Task Learning (2017, methodology): “In [110], a latent probit model is proposed to generate data of different tasks in different feature spaces via sparse transformations on a shared latent space and then to generate labels based on this latent space.”
• Jointly Learning Heterogeneous Features for RGB-D Activity Recognition (2015, background): “Our work is also relevant to heterogeneous features learning methods [9], [46], [53], which were mainly developed for fusing features in generic visual recognition tasks with different assumptions.”
• Autonomous Cross-Domain Knowledge Transfer in Lifelong Policy Gradient Reinforcement Learning (2015, methodology): “In contrast to these methods, our approach provides a computationally efficient mechanism for transfer between multiple task domains, enabling cross-domain lifelong RL. Cross-domain MTL has also been explored in a limited fashion in supervised settings [Han et al., 2012] .”