![Geometry and Computation in High Dimensions.png](/sites/default/files/styles/workshop_banner_sm_1x/public/2023-05/Geometry%20and%20Computation%20in%20High%20Dimensions.png.jpg?itok=1JtiYLWR)
Abstract
Let H be a concept class. We prove that H can be PAC-learned by an (approximate) differentially-private algorithm if and only if it has a finite Littlestone dimension. This implies an equivalence between online learnability and private PAC learnability.