From the 1 of 5 linked papers with an AI index.
1 citations · 1 across the 2 of their papers we have counts for
5 papers
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication
Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari +5
The paper analyzes the theoretical limits of Local SGD for distributed learning with heterogeneous data, showing existing heterogeneity assumptions are insufficient for proving its…
Uniform-in-Time Weak Propagation-of-Chaos in Shallow Neural Networks
Margalit Glasgow, Joan Bruna
We consider one-hidden layer neural networks trained in the feature-learning regime using gradient descent, and relate the output of the finite-width network to it…
Propagation of Chaos in One-hidden-layer Neural Networks beyond Logarithmic Time
Margalit Glasgow, Denny Wu, Joan Bruna
We study the approximation gap between the dynamics of a polynomial-width neural network and its infinite-width counterpart, both trained using projected gradient descent in the me…
Convergence of Distributed Adaptive Optimization with Local Updates
Ziheng Cheng, Margalit Glasgow
We study distributed adaptive algorithms with local updates (intermittent communication). Despite the great empirical success of adaptive methods in distributed training of modern…
A central limit theorem for the matching number of a sparse random graph
Margalit Glasgow, Matthew Kwan, Ashwin Sah +1
In 1981, Karp and Sipser proved a law of large numbers for the matching number of a sparse ErdÅs-Rényi random graph, in an influential paper pioneering the so-called differential…