# Efficient and Scalable Multi-task Regression on Massive Number of Tasks

arXiv: https://arxiv.org/abs/1811.05695 · HTML: https://mlsanity.com/paper/1811.05695/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2018.

## Built on by (most cited first, 9 of 9)

- [OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting](https://mlsanity.com/paper/2304.01506/) (2023)
- [Principle of Relevant Information for Graph Sparsification](https://mlsanity.com/paper/2206.00118/) (2022)
- [Task Grouping for Automated Multi-Task Machine Learning via Task Affinity Prediction](https://mlsanity.com/paper/2310.16241/) (2023)
- [PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression](https://mlsanity.com/paper/2203.12595/) (2022)
- [Multi-Task Learning Regression via Convex Clustering](https://mlsanity.com/paper/2304.13342/) (2023)
- [Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimation](https://mlsanity.com/paper/2312.07952/) (2023)
- [Multi-task learning via robust regularized clustering with non-convex group penalties](https://mlsanity.com/paper/2404.03250/) (2024)
- [Compressive Modeling and Visualization of Multivariate Scientific Data using Implicit Neural Representation](https://mlsanity.com/paper/2510.15535/) (2025)
- [Cascaded Transfer: Learning Many Tasks under Budget Constraints](https://mlsanity.com/paper/2601.21513/) (2026)

## Abstract (arXiv)

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.