2 papers
stat.ML2026
Revisiting Incremental Stochastic Majorization-Minimization Algorithms with Applications to Mixture of Experts
TrungKhang Tran, TrungTin Nguyen, Gersende Fort +5
Processing high-volume, streaming data is increasingly common in modern statistics and machine learning, where batch-mode algorithms are often impractical because they require repe…
cs.LG2025
Federated Majorize-Minimization: Beyond Parameter Aggregation
Aymeric Dieuleveut, Gersende Fort, Mahmoud Hegazy +1
This paper proposes a unified approach for designing stochastic optimization algorithms that robustly scale to the federated learning setting. Our work studies a class of Majorize-…