Summary of Hyperparameter Optimization Can Even Be Harmful in Off-policy Learning and How to Deal with It, by Yuta Saito et al.
Hyperparameter Optimization Can Even be Harmful in Off-Policy Learning and How to Deal with It
by Yuta Saito, Masahiro Nomura
First submitted to arxiv on: 23 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 The paper explores hyperparameter optimization (HPO) for off-policy learning, a crucial step in developing effective policies for applications like recommender systems and personalized medicine. The authors highlight the limitations of current estimators, which can be misled by biased logged data and overestimate generalization performance. They propose simple corrections to the typical HPO procedure to address these issues, demonstrating the effectiveness of their approach through empirical investigations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to find the best settings for a machine learning model that makes decisions based on old data. This is important for things like recommending products or choosing medicines for people. Right now, we have ways to check if a decision-making policy is good, but they can be fooled by biased information. The authors show that this can cause problems and then suggest simple fixes to make the process work better. |
Keywords
» Artificial intelligence » Generalization » Hyperparameter » Machine learning » Optimization
