From the 1 of 24 linked papers with an AI index.
21 papers · 1 filter
What's in a Smoothness Constant? Tighter Rates for Local SGD with Bounded Second-order Heterogeneity
Kumar Kshitij Patel, Rustem Islamov, Sebastian U Stich +3
The paper establishes tighter convergence rates for Local SGD (Federated Averaging) on general convex problems under a bounded second‑order heterogeneity assumption, and provides n…
Why Do We Need Warm-up? A Theoretical Perspective
Foivos Alimisis, Rustem Islamov, Aurelien Lucchi
Learning rate warm-up -- increasing the learning rate at the beginning of training -- has become a ubiquitous heuristic in modern deep learning, yet its theoretical foundations rem…
Beyond a Single Explanation of the Adam--SGD Gap
Chenxiang Zhang, Rustem Islamov, Enea Monzio Compagnoni +3
Prior work has identified several factors that can contribute to the performance gap between Adam and SGD, spanning data aspects, architecture design, and optimization properties.…
Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions
Rustem Islamov, Grigory Malinovsky, Alexander Gaponov +3
Federated Learning (FL) enables heterogeneous clients to collaboratively train a shared model without centralizing their raw data, offering an inherent level of privacy. However, g…
Where You Place the Norm Matters: From Prejudiced to Neutral Initializations
Emanuele Francazi, Francesco Pinto, Aurelien Lucchi +1
Normalization layers were introduced to stabilize and accelerate training, yet their influence is critical already at initialization, where they shape signal propagation and output…
On the Role of Batch Size in Stochastic Conditional Gradient Methods
Rustem Islamov, Roman Machacek, Aurelien Lucchi +3
We study the role of batch size in stochastic conditional gradient methods under a -Kurdyka-Åojasiewicz (-KL) condition. Focusing on momentum-based stochastic conditional…