Summary of Coupled Input-output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis, by Qiao Chen et al.
Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis
by Qiao Chen, Elise Arnaud, Ricardo Baptista, Olivier Zahm
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 A new method for joint dimension reduction is introduced to reduce both input and output spaces of high-dimensional functions. Unlike conventional methods that focus on reducing one space at a time, this coupled approach considers simultaneous reduction and supports goal-oriented dimension reduction where an input or output quantity of interest is prescribed. The method is applied to sensor placement and sensitivity analysis, which involve combinatorial optimization problems with expensive objectives such as expected information gain and Sobol indices. Gradient-based bounds are optimized to determine the most informative sensors and sensitive parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to reduce the size of a big function has been developed. This method reduces both the input and output spaces at the same time, which is important for some applications. For example, it can be used to choose the best places to put sensors or to find the most important things that affect an outcome. The approach uses optimization techniques to solve difficult problems with many possible solutions. |
Keywords
* Artificial intelligence * Optimization
