S&DS677: Topics in High-Dimensional Statistics and Information TheoryYihong Wu, Yale University, Spring 2024
The interplay between information theory and statistics is a constant theme in the development of both fields. This course will discuss how techniques rooted in information theory play a key role in understanding the fundamental limits of high-dimensional statistical problems in terms of minimax risk and sample complexity. In particular, we will rigorously justify the phenomena of dimensionality reduction by either intrinsic low-dimensionality (sparsity, smoothness, shape, etc) or - the less familiar - extrinsic low-dimensionality (functional estimation). Complementing this objective of understanding the fundamental limits, another significant direction is to develop computationally efficient procedures that attain the statistical optimality, or to understand the lack thereof.
Announcements
|