Summary of Gradient Aligned Regression Via Pairwise Losses, by Dixian Zhu et al.
Gradient Aligned Regression via Pairwise Losses
by Dixian Zhu, Tianbao Yang, Livnat Jerby
First submitted to arxiv on: 8 Feb 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 The proposed method, Gradient Aligned Regression (GAR), is a novel approach to regression that leverages label similarity by introducing two pairwise label difference losses. Unlike existing methods that impose extra regularization on the latent feature space, GAR operates in the label space and enjoys the same level of efficiency as conventional regression loss while providing theoretical insights into learning the gradient of the ground truth function. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GAR is a new way to do regression that uses similarities between labels. It’s different from other methods that try to make the model’s predictions more like the real answers by adding extra rules in the hidden layer. GAR works directly with the label space and doesn’t need any special regularization. This makes it as fast as regular regression, but also helps us understand how our model is learning. The authors tested this method on some fake data and eight real-world tasks from six different datasets, comparing it to eight other methods. They found that GAR was faster and worked better. |
Keywords
* Artificial intelligence * Regression * Regularization
