1 citations · 1 across the 14 of their papers we have counts for
5 papers · 1 filter
Fed-BRDECS: Privacy-Preserving and Heterogeneity-Aware Federated Deep Embedded Clustering
Haemin Park, Diego Klabjan, Martin W. Braun +2
Federated deep clustering seeks to learn clustering-friendly representations from decentralized unlabeled data while preserving client privacy. However, Deep Embedded Clustering (D…
Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness
Siqiao Mu, Diego Klabjan
We establish the first convergence guarantees of gradient descent for general feedforward neural networks of any width or depth, with any initialization or dataset. We only assume…
Class-Grouped Normalized Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learning
Haemin Park, Diego Klabjan, Martin W. Braun +2
Class imbalance poses a critical challenge in federated learning (FL), where underrepresented classes suffer from poor predictive performance yet cannot be addressed by standard ce…
Hierarchical Graph Learning for Calendar Spread Strategies in Commodity Futures Markets
Yoonsik Hong, Diego Klabjan
Commodity futures can be represented hierarchically, with underlying assets at the upper level and individual futures contracts at the lower level. Entities at each level can be co…
Rank-Accuracy Trade-off for LoRA: A Gradient-Flow Analysis
Michael Rushka, Diego Klabjan
Previous empirical studies have shown that LoRA achieves accuracy comparable to full-parameter methods on downstream fine-tuning tasks, even for rank-1 updates. By contrast, the th…