Summary of From Function to Distribution Modeling: a Pac-generative Approach to Offline Optimization, by Qiang Zhang et al.
From Function to Distribution Modeling: A PAC-Generative Approach to Offline Optimization
by Qiang Zhang, Ruida Zhou, Yang Shen, Tie Liu
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 challenge of offline optimization, where you have a collection of data examples but don’t know the objective function. Most previous work has focused on learning a surrogate for the unknown objective and then applying existing optimization algorithms. In contrast, this paper takes a more direct approach by viewing optimization as a process of sampling from a generative model. To learn an effective generative model, the authors use the re-weighting technique and derive a probably approximately correct (PAC) lower bound on the natural optimization objective. This allows them to jointly learn a weight function and a score-based generative model. The proposed approach is demonstrated to be robustly competitive using standard offline optimization benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make computers optimize things without knowing what they’re optimizing for. Usually, we try to figure out what the goal is and then use special algorithms to get there. But sometimes it’s hard to do that. Instead of trying to learn what the goal is, this paper says we should just think of optimization as a way to generate new examples. To make this work, we need a good model for generating those examples. The authors came up with a new way to learn this model and tested it on some standard benchmarks. |
Keywords
* Artificial intelligence * Generative model * Objective function * Optimization
