Margalit Glasgow

According to our database, Margalit Glasgow authored at least 16 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Uniform-in-Time Weak Propagation-of-Chaos in Shallow Neural Networks.

Margalit Glasgow, Joan Bruna

CoRR, May, 2026

2025

Propagation of Chaos in One-hidden-layer Neural Networks beyond Logarithmic Time.

Margalit Glasgow, Denny Wu, Joan Bruna

CoRR, April, 2025

Convergence of Distributed Adaptive Optimization with Local Updates.

Ziheng Cheng, Margalit Glasgow

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Mean-field analysis of polynomial-width two-layer neural network beyond finite time horizon.

Margalit Glasgow, Denny Wu, Joan Bruna

Proceedings of the Thirty Eighth Annual Conference on Learning Theory, 2025

2024

On the rank, Kernel, and core of sparse random graphs.

Patrick Demichele, Margalit Glasgow, Alexander Moreira

Random Struct. Algorithms, 2024

SGD Finds then Tunes Features in Two-Layer Neural Networks with near-Optimal Sample Complexity: A Case Study in the XOR problem.

Margalit Glasgow

Proceedings of the Twelfth International Conference on Learning Representations, 2024

The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication.

Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U. Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time.

Arvind V. Mahankali, Jeff Z. HaoChen, Kefan Dong, Margalit Glasgow, Tengyu Ma

CoRR, 2023

Lower Bounds for γ-Regret via the Decision-Estimation Coefficient.

Margalit Glasgow, Alexander Rakhlin

CoRR, 2023

Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning.

Alex Tamkin, Margalit Glasgow, Xiluo He, Noah D. Goodman

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time.

Arvind V. Mahankali, Haochen Zhang, Kefan Dong, Margalit Glasgow, Tengyu Ma

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Max-Margin Works while Large Margin Fails: Generalization without Uniform Convergence.

Margalit Glasgow, Colin Wei, Mary Wootters, Tengyu Ma

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective.

Margalit R. Glasgow, Honglin Yuan, Tengyu Ma

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Asynchronous Distributed Optimization with Stochastic Delays.

Margalit R. Glasgow, Mary Wootters

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Approximate Gradient Coding with Optimal Decoding.

Margalit Glasgow, Mary Wootters

Proceedings of the IEEE International Symposium on Information Theory, 2021

2020

Asynchronous Distributed Optimization with Randomized Delays.

Margalit Glasgow, Mary Wootters

CoRR, 2020