Summary of A Fast Convergence Theory For Offline Decision Making, by Chenjie Mao et al.
A Fast Convergence Theory for Offline Decision Making
by Chenjie Mao, Qiaosheng Zhang
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents a unified approach to offline decision-making problems, including reinforcement learning and off-policy evaluation as special cases. The authors introduce the Decision Making with Offline Feedback (DMOF) framework, which captures a wide range of offline decision-making problems. Within this framework, they propose the Empirical Decision with Divergence (EDD) algorithm, whose upper bound is tied to the Empirical Offline Estimation Coefficient (EOEC). EOEC measures the correlation between the problem and dataset size, reducing at a rate of 1/N as the dataset grows. This guarantees fast convergence for EDD. The authors also provide a lower bound in the DMOF framework, demonstrating the soundness of their theory. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline decision-making problems are crucial in many areas, such as self-driving cars and personalized medicine. This paper shows how to make decisions quickly and accurately using data from past experiences. It proposes a new algorithm called EDD that works well even when there’s not enough data. The authors also provide a way to measure the quality of this algorithm. |
Keywords
» Artificial intelligence » Reinforcement learning
