The paper shows that if a function class is rho-separated, a Gaussian-perturbed follow-the-leader algorithm achieves small-loss regret and differentially private learning rates using an ERM oracle.
Private PAC learning implies finite littlestone dimension
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Small Loss Bounds for Online Learning Separated Function Classes: A Gaussian Process Perspective
The paper shows that if a function class is rho-separated, a Gaussian-perturbed follow-the-leader algorithm achieves small-loss regret and differentially private learning rates using an ERM oracle.