# Multi-Task Learning with Summary Statistics

arXiv: https://arxiv.org/abs/2307.02388 · HTML: https://mlsanity.com/paper/2307.02388/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 5 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Few-Shot Learning via Learning the Representation, Provably](https://mlsanity.com/paper/2002.09434/) (2020)
- [Provable Meta-Learning of Linear Representations](https://mlsanity.com/paper/2002.11684/) (2020)
- [Adaptive and Robust Multi-Task Learning](https://mlsanity.com/paper/2202.05250/) (2022)
- [Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness](https://mlsanity.com/paper/2303.17765/) (2023)
- [Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach](https://mlsanity.com/paper/2108.12112/) (2021)
- [Robust angle-based transfer learning in high dimensions](https://mlsanity.com/paper/2210.12759/) (2022)
- [Heterogeneity-aware and communication-efficient distributed statistical inference](https://mlsanity.com/paper/1912.09623/) (2019)
- [Estimation and Inference with Proxy Data and its Genetic Applications](https://mlsanity.com/paper/2201.03727/) (2022)

## Built on by (most cited first, 7 of 7)

- [On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift](https://mlsanity.com/paper/2312.15551/) (2023)
- [Learning with Shared Representations: Statistical Rates and Efficient Algorithms](https://mlsanity.com/paper/2409.04919/) (2024)
- [Sharp Gaussian approximations for Decentralized Federated Learning](https://mlsanity.com/paper/2505.08125/) (2025)
- [Small transformer architectures for task switching](https://mlsanity.com/paper/2508.04461/) (2025)
- [Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis](https://mlsanity.com/paper/2406.07991/) (2024)
- [Adaptive Multi-task Learning for Multi-sector Portfolio Optimization](https://mlsanity.com/paper/2507.16433/) (2025)
- [Beyond Centralization: Provable Communication Efficient Decentralized Multi-Task Learning](https://mlsanity.com/paper/2512.22675/) (2025)

## Abstract (arXiv)

Multi-task learning has emerged as a powerful machine learning paradigm for integrating data from multiple sources, leveraging similarities between tasks to improve overall model performance. However, the application of multi-task learning to real-world settings is hindered by data-sharing constraints, especially in healthcare settings. To address this challenge, we propose a flexible multi-task learning framework utilizing summary statistics from various sources. Additionally, we present an adaptive parameter selection approach based on a variant of Lepski's method, allowing for data-driven tuning parameter selection when only summary statistics are available. Our systematic non-asymptotic analysis characterizes the performance of the proposed methods under various regimes of the sample complexity and overlap. We demonstrate our theoretical findings and the performance of the method through extensive simulations. This work offers a more flexible tool for training related models across various domains, with practical implications in genetic risk prediction and many other fields.