High Dimensional Robust Sparse Regression

Explore a comprehensive lecture on high-dimensional robust sparse regression presented by Constantine Caramanis from the University of Texas at Austin. Delve into advanced statistical techniques for handling large-scale datasets with sparse structures while maintaining robustness against outliers and noise. Learn about cutting-edge algorithms and methodologies used in sublinear algorithms and nearest-neighbor search, gaining valuable insights into their applications in machine learning and data analysis. Enhance your understanding of this crucial topic in modern statistics and its implications for big data processing and analysis.