Summary of Additive-effect Assisted Learning, by Jiawei Zhang et al.
Additive-Effect Assisted Learning
by Jiawei Zhang, Yuhong Yang, Jie Ding
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a two-stage assisted learning architecture for agents to collaborate on modeling tasks while respecting privacy constraints. The first stage involves a hypothesis testing-based screening method for an agent (Alice) to decide whether to seek assistance from another agent (Bob). This requires minimal data transmission and ensures that only useful information is shared. In the second stage, Alice and Bob jointly apply an iterative model training procedure with limited transmissions of summary statistics. The authors demonstrate that this approach can achieve oracle performance, equivalent to centralized training, while respecting privacy constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps researchers share datasets without sharing sensitive information. Imagine you’re working on a project and need help from someone else, but you don’t want to give away too much information. This problem is common in machine learning, where people work together to improve their models. The authors suggest a way for agents to collaborate while keeping their data private. They propose two stages: first, they test if the other agent’s data is useful, and then they work together using a special training method that doesn’t require sharing too much information. This approach can help people achieve better results without compromising their privacy. |
Keywords
» Artificial intelligence » Machine learning
