Exploring Half Spaces and Robust Statistics

This chapter focuses on the application of Stochastic Gradient Descent (SGD) to optimize half spaces in Euclidean space, highlighting its successes and limitations. It discusses advances in high-dimensional robust statistics, particularly in relation to data poisoning, and emphasizes the theoretical implications for machine learning applications. The conversation wraps up with an examination of the complexities of current algorithms and the importance of reformulating problems to enhance their solvability.