Journal of Optimization Theory and Applications
Neural Computation
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Disease progression modeling from historical clinical databases
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Trading convexity for scalability
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Convex multi-task feature learning
Machine Learning
A convex formulation for learning shared structures from multiple tasks
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning structural SVMs with latent variables
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Recovering sparse signals with a certain family of nonconvex penalties and DC programming
IEEE Transactions on Signal Processing
An efficient algorithm for a class of fused lasso problems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A multi-task learning formulation for predicting disease progression
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
FeaFiner: biomarker identification from medical data through feature generalization and selection
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Alzheimer's Disease (AD) is the most common neurodegenerative disorder associated with aging. Understanding how the disease progresses and identifying related pathological biomarkers for the progression is of primary importance in Alzheimer's disease research. In this paper, we develop novel multi-task learning techniques to predict the disease progression measured by cognitive scores and select biomarkers predictive of the progression. In multi-task learning, the prediction of cognitive scores at each time point is considered as a task, and multiple prediction tasks at different time points are performed simultaneously to capture the temporal smoothness of the prediction models across different time points. Specifically, we propose a novel convex fused sparse group Lasso (cFSGL) formulation that allows the simultaneous selection of a common set of biomarkers for multiple time points and specific sets of biomarkers for different time points using the sparse group Lasso penalty and in the meantime incorporates the temporal smoothness using the fused Lasso penalty. The proposed formulation is challenging to solve due to the use of several non-smooth penalties. We show that the proximal operator associated with the proposed formulation exhibits a certain decomposition property and can be computed efficiently; thus cFSGL can be solved efficiently using the accelerated gradient method. To further improve the model, we propose two non-convex formulations to reduce the shrinkage bias inherent in the convex formulation. We employ the difference of convex programming technique to solve the non-convex formulations. Our extensive experiments using data from the Alzheimer's Disease Neuroimaging Initiative demonstrate the effectiveness of the proposed progression models in comparison with existing methods for disease progression. We also perform longitudinal stability selection to identify and analyze the temporal patterns of biomarkers in disease progression.