Multi-task Sparse Structure Learning with Gaussian Copula Models

By André Ricardo Gonçalves, F. J. Zuben, Arindam Banerjee. 2016

63 citations recorded by the source corpus.

How researchers see this paper

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

1. Qiang Yang
2. Ameet Talwalkar
3. Virginia Smith
4. Yu Zhang
5. Tian Li
6. Yutao Huang
7. Maziar Sanjabi
8. Lingyang Chu

Citations in the corpus

• A Survey on Multi-Task Learning (2017, background): “regression problems, where each output is treated as a task and all the tasks share the training data, are investigated in [92], [94], [95], [96] to not only learn the relations among different outputs/tasks”
• Federated Multi-Task Learning (2017, methodology): “For the graphical model formulation, the problem of solving for Ω is called sparse precision matrix estimation or graphical lasso [15].”
• Personalized Federated Learning: An Attentive Collaboration Approach (2020, background): “Multi-task learning aims to train personalized models for multiple related tasks in a centralized manner by either assuming the structure of the relationships between tasks to be known apriori [26, 27, 28, 29], or attempting to learn the relationships between tasks from data [30, 31, 32].”
• Online Multitask Relative Similarity Learning (2017, unknown): “Specifi-cally, we propose a simple yet effective margin-based query strategy [Cesa-Bianchi et al. , 2006], which has been successfully used for active learning [Cesa-Bianchi et al. , 2006; Hao et al. , 2015; 2016].”