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大会报告
报告1：Stochastic and Distributionally Robust Optimization for Pure Characteristics Demand Models
2024-10-21
报告2：Stochastic gradient methods for solving a class of composite stochastic minimax problems
2024-10-21
报告3：Actor-Critic Reinforcement Learning Algorithms for Mean Field Games in Continuous time, State and Action Spaces
2024-10-21
报告4：Universal Gradient Descent Ascent Method for Smooth Minimax Optimization
2024-10-21
报告5： GPU架构下的数学规划算法
2024-10-21
报告6：Federated Learning on Riemannian Manifolds with Differential Privacy
2024-10-21
报告7：On the Global R-linear Convergence of NAG Method and Beyond
2024-10-21
报告8：Accelerated Decentralized Gradient Method Over Unbalanced Directed Graphs
2024-10-21
报告9：Local Geometry Determines Global Landscape in Low-Rank Factorization for Synchronization
2024-10-21
报告10：Discretization and Quantification for Distributionally Robust Optimization with Decision-Dependent Ambiguity Sets
2024-10-21
报告11：基于SLeM学习框架的学习率自设定方法
2024-10-21
报告13：Understanding and Improving LLM Training: Insights into Adam and Advent of Adam-mini
2024-10-21
报告14：Accelerated Gradient Descent by Concatenation of Stepsize Schedules
2024-10-21
报告15：Inexact Newton Methods for Solving Generalized Equations on Riemannian Manifolds
2024-10-21
报告16：Penalty Decomposition Methods for Second-Best Congestion Pricing Problems on Large-Scale Networks
2024-10-21
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