12151_differentially_private_general.pdf

Hence, the function over this constraint set isG-Lipschitz. Finally, in Lemma6, we provide bounds on excess empirical risk and average regret of gradient descent. Let ℓ be a non-negative eH smooth convex loss function. Let bw:= A(S), S(i) be the dataset where thei-th data point is replaced by an i.i.d. A.4 HighDimensionProofofTheorem 2. Let α 1 be a parameter to be set later.