SOURCE-LINKED INTELLIGENCE
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 concentr
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T19:34:30.000Z
First collected: 2026-09-23T17:51:24.264Z. This is not the publication date.