Gradient Equilibrium in Online Learning

Gradient Equilibrium in Online Learning is a novel concept introduced in the paper "Gradient Equilibrium in Online Learning: Theory and Applications" by Anastasios N. Angelopoulos, Michael I. Jordan, and Ryan J. Tibshirani. It provides a new perspective on online learning by focusing on the convergence of gradient updates over time, rather than traditional metrics like regret minimization.