The Sample Complexity of Learning Linear Predictors with the Squared Loss

In this short note, we provide tight sample complexity bounds for learning linear predictors with respect to the squared loss. Our focus is on an agnostic setting, where no assumptions are made on the data distribution. This contrasts with standard results in the literature, which either make distributional assumptions, refer to specific parameter settings, or use other performance measures.