Summary of Rejection Via Learning Density Ratios, by Alexander Soen et al.
Rejection via Learning Density Ratios
by Alexander Soen, Hisham Husain, Philip Schulz, Vu Nguyen
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 In a novel approach to classification, models can now abstain from making predictions in situations where they’re unsure. Rather than simply tweaking traditional loss functions, this paper proposes a new perspective by finding an idealized data distribution that maximizes a pre-trained model’s performance. This is achieved through the optimization of a loss risk with a -divergence regularization term. By leveraging this idealized distribution and density ratio calculations, models can make informed rejection decisions. Our framework is tested empirically on clean and noisy datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Classification just got smarter! Instead of always making predictions, machines can now decide not to when they’re unsure. This new way of thinking about classification is called “classification with rejection.” It’s like having a “not sure” button. The idea is to find the perfect data distribution that makes your pre-trained model perform best. Then, you can use that to help make smart decisions about when to reject predictions. We tested this on real datasets and it worked well. |
Keywords
» Artificial intelligence » Classification » Optimization » Regularization
