Summary of Generalizing to Any Diverse Distribution: Uniformity, Gentle Finetuning and Rebalancing, by Andreas Loukas et al.
Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing
by Andreas Loukas, Karolis Martinkus, Ed Wagstaff, Kyunghyun Cho
First submitted to arxiv on: 8 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 A novel approach to developing machine learning models that can generalize well to diverse test distributions, even when they deviate significantly from the training data, is presented in this paper. The authors adopt a conservative perspective by accounting for the worst-case error across all sufficiently diverse test distributions within a known domain. They show that training on a uniform distribution over this domain is optimal and provide practical remedies when uniform samples are unavailable. The authors also explore the role of entropy and rebalancing for out-of-distribution generalization and foundation model training, providing mathematical grounding and new empirical evidence across various tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can struggle to work well in situations that are different from how they were trained. This paper looks at ways to make sure models do a good job even when the test data is very different from the training data. The authors suggest that instead of trying to assume what the test data will be like, it’s better to prepare for the worst-case scenario and train on a mix of data that covers all possibilities. They also show that fine-tuning and rebalancing can help when the training data isn’t uniform. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Grounding » Machine learning
