Program for stream Optimization for learning

WC-05: Optimization for learning I
Stream: Optimization for learning
Room: M:N
Chair(s): Manu Upadhyaya

• Incorporating History and Deviations in Forward-Backward Splitting
  Pontus Giselsson
• Optimal Acceleration for Minimax and Fixed-Point Problems is Not Unique
  TaeHo Yoon, Jaeyeon Kim, Jaewook Suh, Ernest Ryu
• Accelerated Algorithms For Nonlinear Matrix Decomposition With The Relu Function
  Giovanni Seraghiti, Arnaud Vandaele, Margherita Porcelli, Nicolas Gillis

WD-05: Optimization for learning II
Stream: Optimization for learning
Room: M:N
Chair(s): Manu Upadhyaya

• Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization
  Hanmin Li, Avetik Karagulyan, Peter Richtarik
• Optimization flows landing on the Stiefel manifold: continuous-time flows, deterministic and stochastic algorithms
  Bin Gao, P.-A. Absil
• Is maze-solving parallelizable?
  Romain Cosson

TB-05: Optimization for learning III
Stream: Optimization for learning
Room: M:N
Chair(s): Max Nilsson

• MAST: Model-Agnostic Sparsified Training
  Egor Shulgin, Peter Richtarik
• Online Learning and Information Exponents: The Importance of Batch size & Time/Complexity Tradeoffs
  Stephan Ludovic
• Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning
  Constantin Philippenko, Aymeric Dieuleveut