Margalit Glasgow

Orcid: 0000-0001-6605-2127

According to our database1, Margalit Glasgow authored at least 12 papers between 2020 and 2024.

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Bibliography

2024
Convergence of Distributed Adaptive Optimization with Local Updates.
CoRR, 2024

SGD Finds then Tunes Features in Two-Layer Neural Networks with near-Optimal Sample Complexity: A Case Study in the XOR problem.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication.
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.
CoRR, 2023

Lower Bounds for γ-Regret via the Decision-Estimation Coefficient.
CoRR, 2023

Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning.
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.
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.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Asynchronous Distributed Optimization with Stochastic Delays.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Approximate Gradient Coding With Optimal Decoding.
IEEE J. Sel. Areas Inf. Theory, 2021

2020
Asynchronous Distributed Optimization with Randomized Delays.
CoRR, 2020


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