![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
We present lower-bounds for the generalization error of gradient descent on free initializations, reducing the problem to testing the algorithm’s output under different data models. We then discuss lower-bounds on random initialization and present the problem of learning communities in the pruned-block-model, where it is conjectured that GD fails.