Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective
The paper explores implicit bias—how models select specific solutions among many with identical performance—by framing it as a geometric correction caused by noise and continuous symmetries in training dynamics. It develops a framework where this bias emerges from mapping parameter space onto predictor space, leading to an effective loss function that can be engineered for inverse design.