3 papers
cs.LG2026
Clipping Makes Distributed and Federated Asynchronous SGD Robust to Stragglers
Samuel Erickson, Mikael Johansson
In modern machine learning, parallelization of training is an important strategy for increasing scale. Asynchronous stochastic gradient descent (ASGD), which maximizes the utilizat…
cs.LG2025
Personalized Federated Learning under Model Dissimilarity Constraints
Samuel Erickson, Mikael Johansson
One of the defining challenges in federated learning is that of statistical heterogeneity among clients. We address this problem with KARULA, a regularized strategy for personalize…
stat.ML2025
Inverse Covariance and Partial Correlation Matrix Estimation via Joint Partial Regression
Samuel Erickson, Tobias Rydén
We present a method for estimating sparse high-dimensional inverse covariance and partial correlation matrices, which exploits the connection between the inverse covariance matrix…