Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime Apple Machine Learning Research
*=Equal Contributors We consider online learning problems in the realizable setting, where there is a zero-loss solution, and propose new Differentially Private (DP) algorithms that obtain near-optimal regret bounds. For the problem of online prediction from experts, we design new algorithms that obtain near-optimal regret… Read More »Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime Apple Machine Learning Research