Summary of Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning, by Philip Amortila et al.
Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning
by Philip Amortila, Tongyi Cao, Akshay Krishnamurthy
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC)
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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 the problem of distribution shift in machine learning, where training and deployment conditions differ. The authors focus on L_{}-misspecified regression and adversarial covariate shift, exploring how standard least squares regression can amplify errors due to misspecification. To mitigate this issue, they propose a new algorithm inspired by robust optimization techniques, which achieves optimal statistical rates while avoiding misspecification amplification. The authors demonstrate the effectiveness of their approach in offline and online reinforcement learning with misspecification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models often struggle when deployed in environments different from those they were trained on. This is because training and deployment conditions can differ, causing a performance drop. Researchers have developed algorithms to combat this problem, but it’s not fully understood how these algorithms perform when the model doesn’t perfectly fit the data. In this study, scientists investigate what happens when a model is designed for one type of data, but used with different types. They show that standard methods can actually make things worse by increasing errors due to the difference in data. To fix this issue, they introduce a new algorithm that works better and doesn’t amplify these errors. |
Keywords
* Artificial intelligence * Machine learning * Optimization * Regression * Reinforcement learning
