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Found 5 papers, 2 papers with code
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
The interest in federated learning has surged in recent research due to its unique ability to train a global model using privacy-secured information held locally on each client.
On the Hardness of Robustness Transfer: A Perspective from Rademacher Complexity over Symmetric Difference Hypothesis Space
Recent studies demonstrated that the adversarially robust learning under $\ell_\infty$ attack is harder to generalize to different domains than standard domain adaptation.
Cutting Some Slack for SGD with Adaptive Polyak Stepsizes
We use this insight to develop new variants of the SPS method that are better suited to nonlinear models.
Towards closing the gap between the theory and practice of SVRG
Among the very first variance reduced stochastic methods for solving the empirical risk minimization problem was the SVRG method (Johnson & Zhang 2013).
Optimal mini-batch and step sizes for SAGA
Using these bounds, and since the SAGA algorithm is part of this JacSketch family, we suggest a new standard practice for setting the step sizes and mini-batch size for SAGA that are competitive with a numerical grid search.
