Summary of Learning Linear Dynamics From Bilinear Observations, by Yahya Sattar et al.
Learning Linear Dynamics from Bilinear Observations
by Yahya Sattar, Yassir Jedra, Sarah Dean
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Optimization and Control (math.OC); 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 tackles the problem of learning a partially observed dynamical system with linear state transitions and bilinear observations. The authors develop a finite-time analysis for learning the unknown dynamics matrices, considering various challenges such as heavy-tailed and dependent data, and design matrix construction. They provide high-probability error bounds for fixed inputs and sample complexity for random inputs, ultimately deriving an upper bound on statistical error rates. The results have implications for learning unknown dynamics matrices from a single finite trajectory of bilinear observations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding patterns in a system that we can only see part of. It’s like trying to figure out how a car works just by looking at the wheels and seeing it drive around. The researchers develop a way to learn the underlying rules of this system from a single observation, even when the data is tricky or noisy. |
Keywords
» Artificial intelligence » Probability
