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	Jun 27, 18:00 - 20:00, Better Methods and Theory for Federated Learning: Compression, Client Selection, and Heterogeneity (B5 L5 R5209), Samuel Horváth, Ph.D. Student, Statistics	Federated learning, Optimization for Machine Learning, PhD Dissertation Defense
	Nov 11, 12:00 - 13:00, Langevin Monte Carlo as an optimization algorithm (B9 L2 H1 R2322), Adil Salim, Postdoctoral Research Fellow, Computer Science	Langevin Monte Carlo, machine learning, optimization, Graduate Seminar
	Jul 30, 16:00 - 18:00, Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters (KAUST), Filip Hanzely, Ph.D., Applied Mathematics and Computational Sciences	PhD Dissertation Defense
	Dec 2, 12:00 - 13:00, Proximal Splitting Methods for Convex Optimization: An Introduction (B9 L2 H1 R2322), Laurent Condat, Senior Research Scientist, Computer, Electrical and Mathematical Sciences and Engineering	Graduate Seminar
	May 30, 11:00 - 14:00, Stein Variational Gradient Descent and Consensus-Based Optimization: Towards a Convergence Analysis and Generalization (B3 L5 R5220), Lukang Sun, Ph.D. Student, Computer Science	PhD Dissertation Defense