Information-theoretic Limits of Learning and Estimation
Information theory plays a central role in establishing fundamental limits on what any learning or estimation algorithm can -- and cannot -- achieve, regardless of computational power. In this chapter, we provide an introduction to these connections. End-of-chapter exercises makes the material suitable for both classroom use and self-study. We begin by introducing concentration inequalities along with the notions of covering and packing in metric spaces, and the associated concept of metric entropy. These tools are essential for our analysis. We then introduce the learning-theoretic framework and derive upper bounds on generalization error in terms of metric entropy, Rademacher complexity...
| Authors | Abbas El Gamal, Maxim Raginsky |
|---|---|
| Primary category | cs.IT |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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