Summary of Efficient Task Grouping Through Samplewise Optimisation Landscape Analysis, by Anshul Thakur et al.
Efficient Task Grouping Through Samplewise Optimisation Landscape Analysis
by Anshul Thakur, Yichen Huang, Soheila Molaei, Yujiang Wang, David A. Clifton
First submitted to arxiv on: 5 Dec 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 This paper addresses the issue of negative transfer in shared training approaches for machine learning applications. The problem occurs when multiple tasks are trained simultaneously using multi-task learning (MTL) or gradient-based meta-learning, leading to performance degradation in specific tasks. To mitigate this issue, the authors introduce an efficient task grouping framework that reduces computational demands by inferring pairwise task similarities through a sample-wise optimisation landscape analysis. This is achieved without requiring shared model training, unlike existing methods. The framework uses a graph-based clustering algorithm to pinpoint near-optimal task groups, solving the originally NP-hard problem efficiently. Experimental results on 8 different datasets demonstrate a five-fold speed enhancement compared to previous state-of-the-art methods, with comparable performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to group tasks together when doing machine learning. Sometimes, we want to do multiple things at once, but that can make things worse for some tasks. The authors found a way to make it better by looking at similarities between tasks and grouping them together. This makes the process faster and more efficient, which is important because computers get very busy with too many tasks! They tested this on 8 different datasets and it worked just as well as other methods, but much faster. |
Keywords
» Artificial intelligence » Clustering » Machine learning » Meta learning » Multi task
