Summary of Functional Graphical Models: Structure Enables Offline Data-driven Optimization, by Jakub Grudzien Kuba et al.
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization
by Jakub Grudzien Kuba, Masatoshi Uehara, Pieter Abbeel, Sergey Levine
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Machine learning models are typically designed for prediction tasks, but they can also be used for optimization problems. For instance, given a dataset of proteins and their corresponding fluorescence levels, we might want to optimize for a new protein with the highest possible fluorescence. This kind of data-driven optimization (DDO) poses unique challenges beyond those in standard prediction problems, as models must successfully predict the performance of new designs that outperform the best designs seen in the training set. It is unclear theoretically when existing approaches can perform better than the naive approach that simply selects the best design in the dataset. This paper studies how structure can enable sample-efficient data-driven optimization. We introduce functional graphical models (FGMs) and show theoretically how they can provide principled data-driven optimization by decomposing the original high-dimensional optimization problem into smaller sub-problems. This allows us to derive more practical regret bounds for DDO, and the result implies that DDO with FGMs can achieve nearly optimal designs in situations where naive approaches fail due to insufficient coverage of offline data. We also present a data-driven optimization algorithm that infers the FGM structure itself, either over the original input variables or a latent variable representation of the inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are great for making predictions, but they can also be used to optimize things! Imagine you have a list of proteins and how bright they glow. You want to find the best protein that glows super brightly. This is called data-driven optimization (DDO). It’s different from regular prediction problems because you need to predict which new designs will work better than what you’ve already seen. The question is, can we use existing methods to do this or do we need something new? In this paper, the authors talk about how using “structure” can help us with DDO. They introduce a new way of thinking called functional graphical models (FGMs) that lets us break down big optimization problems into smaller ones. This helps us come up with better ways to optimize and get closer to finding the best solution. |
Keywords
* Artificial intelligence * Machine learning * Optimization
