*= Equal Contributors
Online prediction from experts is a fundamental problem in machine learning and several works have studied this problem under privacy constraints. We propose and analyze new algorithms for this problem that improve over the regret bounds of the best existing algorithms for non-adaptive adversaries. For approximate differential privacy, our algorithms achieve regret bounds of for the stochastic setting and for oblivious adversaries (where is the number of experts). For pure DP, our algorithms are the first to obtain sub-linear regret for oblivious adversaries in the… *= Equal Contributors
Online prediction from experts is a fundamental problem in machine learning and several works have studied this problem under privacy constraints. We propose and analyze new algorithms for this problem that improve over the regret bounds of the best existing algorithms for non-adaptive adversaries. For approximate differential privacy, our algorithms achieve regret bounds of for the stochastic setting and for oblivious adversaries (where is the number of experts). For pure DP, our algorithms are the first to obtain sub-linear regret for oblivious adversaries in the… Read More