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Title: Gradient-based algorithms in high-dimensions under limited sample complexity
Abstract: I will overview our recent progress on analyzing gradient descent, Langevin algorithms and stochastic gradient descent in simple neural networks under the teacher-student setting. Our particular focus is on the high-dimensional regime where the number of samples is just a constant times the dimension.