Regression Models

Unveiling Insights from "Gradient Descent Converges Linearly for Logistic Regression on Separable Data"

Bang An, Ph.D. Student, Applied Mathematics and Computational Sciences

Jan 17, 10:00 - 11:00

B1 L0 R0118

gradient methods Regression Models

Abstract In this presentation, I will share a paper titled "Gradient Descent Converges Linearly for Logistic Regression on Separable Data", a work highly related to my ongoing research. I will explore its relevance to my current research topic and discuss the inspiration for our future works. Abstract of the paper: We show that running gradient descent with variable learning rate guarantees loss f(x) \leq 1.1f(x^*)+\epsilon for the logistic regression objective, where the error \epsilon decays exponentially with the number of iterations and polynomially with the magnitude of the entries of an