Summary of Generalization Error Bounds For Learning Under Censored Feedback, by Yifan Yang et al.
Generalization Error Bounds for Learning under Censored Feedback
by Yifan Yang, Ali Payani, Parinaz Naghizadeh
First submitted to arxiv on: 14 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 derives extensions to existing generalization error bounds from learning theory to account for non-IIDness due to censored feedback. The authors first develop a DKW inequality for problems with non-IID data, then use this bound to provide a new guarantee on the generalization performance of classifiers trained on such data. They show that existing bounds fail to capture the model’s guarantees in the presence of censored feedback and analyze the effectiveness of exploration techniques in improving these bounds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers are trying to understand how well an algorithm will perform on new data when some data is missing or biased. The authors start by adapting a known mathematical formula (DKW inequality) for cases where the data isn’t evenly distributed. Then, they use this adaptation to create a new way to predict how well a model will work in real-world situations with limited data. They show that older methods don’t work as well when there’s biased data and discuss ways to make these predictions more accurate. |
Keywords
* Artificial intelligence * Generalization
