Summary of Compositional Risk Minimization, by Divyat Mahajan et al.
Compositional Risk Minimization
by Divyat Mahajan, Mohammad Pezeshki, Charles Arnal, Ioannis Mitliagkas, Kartik Ahuja, Pascal Vincent
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 tackles compositional generalization in machine learning, aiming to develop data-efficient intelligent machines that generalize in human-like ways. The researchers propose a simple alternative to empirical risk minimization called compositional risk minimization (CRM) to tackle distribution shift. They model the data with flexible additive energy distributions, where each energy term represents an attribute, and show that CRM extrapolates to special affine hulls of seen attribute combinations. Empirical evaluations on benchmark datasets confirm the improved robustness of CRM compared to other methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching machines to learn in a way that’s similar to how humans do. It’s trying to make machines better at understanding things they haven’t seen before, even if those things are made up of parts they’ve never seen together before. The researchers came up with a new way to train machines called compositional risk minimization (CRM). They tested it on some problems and found that it did better than other methods at figuring out what’s going on in situations it hadn’t seen before. |
Keywords
» Artificial intelligence » Generalization » Machine learning
