Efficient and Scalable Multi-task Regression on Massive Number of Tasks
This addresses scalability issues in multi-task learning for domains like retail and transportation, offering a practical solution for large-scale regression problems.
The paper tackles the challenge of scaling multi-task learning to massive numbers of tasks by proposing CCMTL, which integrates convex clustering and a new optimization method, achieving linear scalability and outperforming seven state-of-the-art methods in accuracy and efficiency, e.g., training in 30 seconds on 23,812 tasks compared to hours or days for others.
Many real-world large-scale regression problems can be formulated as Multi-task Learning (MTL) problems with a massive number of tasks, as in retail and transportation domains. However, existing MTL methods still fail to offer both the generalization performance and the scalability for such problems. Scaling up MTL methods to problems with a tremendous number of tasks is a big challenge. Here, we propose a novel algorithm, named Convex Clustering Multi-Task regression Learning (CCMTL), which integrates with convex clustering on the k-nearest neighbor graph of the prediction models. Further, CCMTL efficiently solves the underlying convex problem with a newly proposed optimization method. CCMTL is accurate, efficient to train, and empirically scales linearly in the number of tasks. On both synthetic and real-world datasets, the proposed CCMTL outperforms seven state-of-the-art (SoA) multi-task learning methods in terms of prediction accuracy as well as computational efficiency. On a real-world retail dataset with 23,812 tasks, CCMTL requires only around 30 seconds to train on a single thread, while the SoA methods need up to hours or even days.