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06/22/2023
The Inductive Bias of Flatness Regularization for Deep Matrix Factorization
Recent works on over-parameterized neural networks have shown that the s...
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01/28/2022
FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients
In classical federated learning, the clients contribute to the overall t...
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05/12/2021
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces
Negative sampling schemes enable efficient training given a large number...
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11/17/2020
Federated Composite Optimization
Federated Learning (FL) is a distributed learning paradigm which scales ...
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02/29/2020
Adaptive Federated Optimization
Federated learning is a distributed machine learning paradigm in which a...
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02/20/2020
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets
We study distributed optimization algorithms for minimizing the average ...
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10/21/2019
