Summary of Automated Discovery Of Pairwise Interactions From Unstructured Data, by Zuheng (david) Xu et al.
Automated Discovery of Pairwise Interactions from Unstructured Data
by Zuheng, Moksh Jain, Ali Denton, Shawn Whitfield, Aniket Didolkar, Berton Earnshaw, Jason Hartford
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 introduces two novel statistical tests for detecting pairwise interactions between perturbations affecting latent variables in systems where observations are low-dimensional, hand-crafted measurements. The tests, based on pairwise interventions, can be integrated into an active learning pipeline to efficiently discover these interactions. The authors demonstrate the value of their approach in biology, where pairwise perturbation experiments are commonly used to reveal hidden dependencies. They validate their method on synthetic and real biological datasets, showing that it outperforms random search and standard active learning baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to figure out how different things affect each other in complex systems. When we have limited information, it’s hard to tell if two things are connected just by looking at one or the other. The researchers came up with two simple tests that can be used to find these connections between pairs of perturbations. They tested their method on some real-life biological data and showed that it works better than other approaches. |
Keywords
» Artificial intelligence » Active learning
