Summary of Domain Generalisation Via Imprecise Learning, by Anurag Singh et al.
Domain Generalisation via Imprecise Learning
by Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet
First submitted to arxiv on: 6 Apr 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 out-of-distribution (OOD) generalization is proposed in this paper, tackling the challenge of deciding between various notions of generalization, such as average-case risk or worst-case risk. The framework introduced, called Imprecise Domain Generalisation, addresses arbitrary commitments to specific generalization strategies by machine learners due to deployment uncertainties. This is achieved through an imprecise risk optimization that allows learners to optimize against a continuous spectrum of generalization strategies during training, and a model framework that enables operators to specify their generalisation preference at deployment. Theoretical and empirical evidence support the benefits of integrating imprecision into domain generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Domain generalization is a challenging problem in machine learning that involves deciding which type of risk to optimize for. This paper proposes a new approach called Imprecise Domain Generalisation that allows machine learners to stay imprecise by optimizing against different types of risks during training. This means that the model can generalize well to new situations without being limited to one specific type of risk. The authors also introduce a new framework that lets operators choose their own level of generalization when deploying the model. |
Keywords
* Artificial intelligence * Domain generalization * Generalization * Machine learning * Optimization
