# Collaborative Prediction: To Join or To Disjoin Datasets

arXiv: https://arxiv.org/abs/2506.11271 · HTML: https://mlsanity.com/paper/2506.11271/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 12 Jun 2025.

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

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Understanding intermediate layers using linear classifier probes](https://mlsanity.com/paper/1610.01644/) (2016)
- [Convergent Learning: Do different neural networks learn the same representations?](https://mlsanity.com/paper/1511.07543/) (2015)
- [Collaborative Performance Prediction for Large Language Models](https://mlsanity.com/paper/2407.01300/) (2024)
- [Collaborative Learning via Prediction Consensus](https://mlsanity.com/paper/2305.18497/) (2023)
- [Feature Representation in Convolutional Neural Networks](https://mlsanity.com/paper/1507.02313/) (2015)
- [Co-Clustering for Multitask Learning](https://mlsanity.com/paper/1703.00994/) (2017)

## Built on by (most cited first, 1 of 1)

- [When to Transfer: Adaptive Source Selection for Positive Transfer in Linear Models](https://mlsanity.com/paper/2510.16986/) (2025)

## Abstract (arXiv)

With the recent rise of generative Artificial Intelligence (AI), the need of selecting high-quality dataset to improve machine learning models has garnered increasing attention. However, some part of this topic remains underexplored, even for simple prediction models. In this work, we study the problem of developing practical algorithms that select appropriate dataset to minimize population loss of our prediction model with high probability. Broadly speaking, we investigate when datasets from different sources can be effectively merged to enhance the predictive model's performance, and propose a practical algorithm with theoretical guarantees. By leveraging an oracle inequality and data-driven estimators, the algorithm reduces population loss with high probability. Numerical experiments demonstrate its effectiveness in both standard linear regression and broader machine learning applications. Code is available at https://github.com/kkrokii/collaborative_prediction.