Summary of Meta-learning with Heterogeneous Tasks, by Zhaofeng Si et al.
Meta-Learning with Heterogeneous Tasks
by Zhaofeng Si, Shu Hu, Kaiyi Ji, Siwei Lyu
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed novel meta-learning method, Heterogeneous Tasks Robust Meta-learning (HeTRoM), addresses the limitation of existing methods assuming equal importance for all tasks. HeTRoM employs rank-based task-level learning objectives to effectively manage heterogeneous tasks, preventing easy tasks from overwhelming the meta-learner. This approach enables an efficient iterative optimization algorithm based on bi-level optimization and statistical guidance. Experimental results show that HeTRoM provides flexibility, allowing adaptation to diverse task settings and enhancing overall performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HeTRoM is a new way for machines to learn quickly when given only a few examples of many different tasks. Most current methods assume all tasks are equal in difficulty, but real-world problems have tasks with varying difficulties or noise. HeTRoM helps by making the learning process more robust to these differences. This means it can be used in many situations where data is limited. |
Keywords
» Artificial intelligence » Meta learning » Optimization
