Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization Grigory Malinovsky, Ph.D. Student, Applied Mathematics and Computational Science Jun 18, 15:45 - 19:00 B5 R5209 machine learning Federated learning distributed optimization applied statistics
First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data Arto Maranjyan, Ph.D. Student, Computer Science Dec 4, 08:30 - 11:00 B4/5 L0 A0215 machine learning optimization asynchronous algorithms Training
The Many Faces of Compression: Theory and Practice in Federated Optimization Elnur Gasanov, Ph.D. Student, Computer Science May 28, 10:00 - 12:00 B3, L5, R5209 compression methods communication efficiency proximal algorithms
Optimization Methods and Software for Federated Learning Konstantin Burlachenko, Ph.D. Student, Computer Science May 8, 19:00 - 21:00 B5 L5 R5209
Stein Variational Gradient Descent and Consensus-Based Optimization: Towards a Convergence Analysis and Generalization Lukang Sun, Ph.D. Student, Computer Science May 30, 11:00 - 14:00 B3 L5 R5220
Better Methods and Theory for Federated Learning: Compression, Client Selection, and Heterogeneity Samuel Horváth, Ph.D. Student, Statistics Jun 27, 18:00 - 20:00 B5 L5 R5209 Federated learning Optimization for Machine Learning
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely, Ph.D., Applied Mathematics and Computational Science Jul 30, 16:00 - 18:00 KAUST
