Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity Full Version Uri Stemmer

The building block for our learner is a new differentially private algorithm for approximately solving the linear feasibility problem: Given a feasible collection of m linear constraints of the form Ax b, the task is to privately identify a solution x that satisfies most of the constraints. Our algorithm is iterative, where each iteration determines the next coordinate of the constructed solution x.