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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：基于厂尝别惭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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