Learning and Control Beyond Linearity: Towards a Non-asymptotic Theory for Bilinear Systems
This tutorial provides a unified view of the emerging area of bilinear learning and control. Using linear systems as a benchmark, it explains what fundamentally changes in the bilinear settings, how recent theory addresses finite-sample learning and control, and how these ideas connect to broader themes in nonlinear control, representation learning, and data-driven decision making. For learning, we emphasize tools that are particularly useful in the bilinear settings, such as one-sided Bernstein's inequality for dependent and heavy-tailed covariates, blocking arguments, and martingale concentration for input-dependent noise. We then apply these tools to obtain finite-sample learning guara...
| Authors | Yahya Sattar, Yassir Jedra, Robin Strässer et al. |
|---|---|
| Primary category | eess.SY |
| Published | 16 September 2026 |
| Last updated at source | 26 September 2026 |
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