15 citations · 30 across the 16 of their papers we have counts for
4 papers · 1 filter
SGD Finds then Tunes Features in Two-Layer Neural Networks with near-Optimal Sample Complexity: A Case Study in the XOR problem
Margalit Glasgow
In this work, we consider the optimization process of minibatch stochastic gradient descent (SGD) on a 2-layer neural network with data separated by a quadratic ground truth functi…
Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time
Arvind Mahankali, Jeff Z. Haochen, Kefan Dong +2
Despite recent theoretical progress on the non-convex optimization of two-layer neural networks, it is still an open question whether gradient descent on neural networks without un…
The Exact Rank of Sparse Random Graphs
Margalit Glasgow, Matthew Kwan, Ashwin Sah +1
Two landmark results in combinatorial random matrix theory, due to Komlós and Costello-Tao-Vu, show that discrete random matrices and symmetric discrete random matrices are typical…
Tight Bounds for -Regret via the Decision-Estimation Coefficient
Margalit Glasgow, Alexander Rakhlin
In this work, we give a statistical characterization of the -regret for arbitrary structured bandit problems, the regret which arises when comparing against a benchmark that is…