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
Error Feedback for Communication-Efficient First and Second-Order Distributed Optimization: Theory and Practical Implementation Konstantin Burlachenko, Ph.D. Student, Computer Science May 12, 12:00 - 13:00 B9 L2 R2325 Federated learning software development
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 Sciences Jul 30, 16:00 - 18:00 KAUST
Proximal Splitting Methods for Convex Optimization: An Introduction Laurent Condat, Senior Research Scientist, Computer, Electrical and Mathematical Sciences and Engineering Dec 2, 12:00 - 13:00 B9 L2 H1 R2322
Laurent Condat, Senior Research Scientist, Computer, Electrical and Mathematical Sciences and Engineering
Langevin Monte Carlo as an optimization algorithm Adil Salim, Postdoctoral Research Fellow, Computer Science Nov 11, 12:00 - 13:00 B9 L2 H1 R2322 machine learning Langevin Monte Carlo optimization
