Summary of A Sparsity Principle For Partially Observable Causal Representation Learning, by Danru Xu et al.
A Sparsity Principle for Partially Observable Causal Representation Learning
by Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes a novel approach to causal representation learning in partially observed settings. Unlike previous methods that assume all latent causal variables are captured in high-dimensional observations, this work considers scenarios where each measurement only provides information about a subset of underlying causal states. The authors establish two identifiability results for this setting, one for linear mixing functions and another for piecewise linear mixing functions with Gaussian latent causal variables. Building on these insights, they propose two methods to estimate the underlying causal variables by enforcing sparsity in the inferred representation. Experimental results on simulated datasets and established benchmarks demonstrate the effectiveness of their approach in recovering ground-truth latents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can figure out what’s causing things to happen based on incomplete data. Usually, when we try to understand why something happened, we need a lot of information. But sometimes we only have a little bit of information, and that makes it harder to know what’s really going on. This paper shows that even with incomplete data, computers can still learn about the underlying causes if they use special techniques. The authors also test their ideas using pretend data and real data from established benchmarks. |
Keywords
* Artificial intelligence * Representation learning
