Margin Halfspaces

Margin halfspaces, linear classifiers defined by a hyperplane with a specified margin, are a central topic in machine learning, with research focusing on efficient and robust learning algorithms in the presence of noise (e.g., Massart, adversarial, random classification noise) and distribution shifts. Current efforts involve developing computationally efficient algorithms, often based on hinge loss minimization, SGD, or sum-of-squares programming, that achieve near-optimal sample complexity and error bounds under various noise conditions and distributional assumptions. These advancements have significant implications for improving the robustness and generalization capabilities of machine learning models in real-world applications where noisy or incomplete data are common.