Arefe Boushehrian

Provable Bounds for the Learnability of Sample-Compressible Families from Noisy Samples

Learning distribution families over $\mathbb{R}^d$ is a fundamental problem in unsupervised learning and statistics. A central question in this setting is whether a given family of distributions possesses sufficient structure to be (at least) information-theoretically learnable and, if so, to charac…