Summary of Acceleratedlingam: Learning Causal Dags at the Speed Of Gpus, by Victor Akinwande et al.
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs
by Victor Akinwande, J. Zico Kolter
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); 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 addresses the scalability issue in existing causal discovery methods, which are hindered by slow combinatorial optimization or search processes. Recent approaches formulate causal discovery as structure learning with continuous optimization but lack statistical guarantees. The authors propose an efficient parallelization strategy to scale these methods, focusing on the LiNGAM method, which is quadratic in the number of variables. By implementing GPU kernels for the causal ordering subprocedure in DirectLiNGAM, they achieve a 32-fold speed-up on benchmark datasets compared to sequential implementations. This enables the application of DirectLiNGAM to large-scale gene expression data and U.S. stock data for causal inference and discovery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with how we discover causes in big data sets. Right now, methods are too slow and can’t handle huge amounts of data. The authors came up with a way to make these methods faster by using computers’ processing power more efficiently. They took an existing method called LiNGAM and made it work much quicker by breaking it down into smaller parts that can be done simultaneously on different computer chips. This allows them to apply this method to really big data sets, like those related to gene expression or stock prices. |
Keywords
* Artificial intelligence * Inference * Optimization
