185 citations · 707 across the 16 of their papers we have counts for
33 papers
Data-heterogeneity-aware Mixing for Decentralized Learning
Yatin Dandi, Anastasia Koloskova, Martin Jaggi +1
Decentralized learning provides an effective framework to train machine learning models with data distributed over arbitrary communication graphs. However, most existing approaches…
Tackling benign nonconvexity with smoothing and stochastic gradients
Harsh Vardhan, Sebastian U. Stich
Non-convex optimization problems are ubiquitous in machine learning, especially in Deep Learning. While such complex problems can often be successfully optimized in practice by usi…
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning
Anastasia Koloskova, Tao Lin, Sebastian U. Stich
We consider decentralized machine learning over a network where the training data is distributed across agents, each of which can compute stochastic model updates on their loca…
Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods
Amirkeivan Mohtashami, Sebastian Stich, Martin Jaggi
While SGD, which samples from the data with replacement is widely studied in theory, a variant called Random Reshuffling (RR) is more common in practice. RR iterates through random…
On Second-order Optimization Methods for Federated Learning
Sebastian Bischoff, Stephan Günnemann, Martin Jaggi +1
We consider federated learning (FL), where the training data is distributed across a large number of clients. The standard optimization method in this setting is Federated Averagin…
Semantic Perturbations with Normalizing Flows for Improved Generalization
Oguz Kaan Yuksel, Sebastian U. Stich, Martin Jaggi +1
Data augmentation is a widely adopted technique for avoiding overfitting when training deep neural networks. However, this approach requires domain-specific knowledge and is often…