Training Neural Networks at Any Scale Volkan Cevher, Associate Professor, School of Engineering, Swiss Federal Institute of Technology (EPFL), Switzerland Nov 24, 12:00 - 13:00 B9 L2 R2325 machine learning Numerical simulation and analysis Reinforcement Learning deep learning optimization
Ringleader ASGD: The First Asynchronous SGD with Optimal Time Complexity under Data Heterogeneity Arto Maranjyan, Ph.D. Student, Computer Science Oct 28, 14:30 - 15:30 B1 L3 R3119
BiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training Laurent Condat, Senior Research Scientist, Computer Science Oct 27, 12:00 - 13:00 B9 L2 R2325 optimization Distributed algorithms Signal and Image Processing
From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs Peter Richtarik, Professor, Computer Science Sep 16, 16:00 - 17:00 B1 L3 R3119 AI machine learning optimization algorithms LLM
From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs Peter Richtarik, Professor, Computer Science Sep 15, 12:00 - 13:00 B9 L2 R2325 AI machine learning optimization algorithms LLM
From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs Peter Richtarik, Professor, Computer Science Sep 4, 12:00 - 13:00 B9 L2 R2325 AI machine learning optimization algorithms LLM
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 Science Jul 30, 16:00 - 18:00 KAUST
Proximal Splitting Methods for Convex Optimization: An Introduction Laurent Condat, Senior Research Scientist, Computer Science Dec 2, 12:00 - 13:00 B9 L2 H1 R2322
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
