Image de couverture de livre

A Mean-Field Analysis of Neural Stochastic Gradient Descent-Ascent for Functional Minimax Optimization

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Zhu, Yuchen, Zhang, Yufeng, Wang, Zhaoran, Yang, Zhuoran, Chen, Xiaohong
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Optimization and Control
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization
par: Shen, Wei, et autres
Publié: (2023)

Decentralized Stochastic Gradient Descent Ascent for Finite-Sum Minimax Problems
par: Gao, Hongchang
Publié: (2022)

Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization
par: Lin, Tianyi, et autres
Publié: (2024)

On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
par: Lin, Tianyi, et autres
Publié: (2019)

Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic
par: Zhang, Yufeng, et autres
Publié: (2021)

Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
par: Zhang, Yufeng, et autres
Publié: (2020)

Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization
par: Yang, Zhuoran, et autres
Publié: (2020)

Sobolev Gradient Ascent for Optimal Transport: Barycenter Optimization and Convergence Analysis
par: Kim, Kaheon, et autres
Publié: (2025)

Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency
par: Cai, Qi, et autres
Publié: (2022)

Provably Efficient Exploration in Policy Optimization
par: Cai, Qi, et autres
Publié: (2019)

Using Stochastic Gradient Descent to Smooth Nonconvex Functions: Analysis of Implicit Graduated Optimization
par: Sato, Naoki, et autres
Publié: (2023)

Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-max Optimization
par: Zheng, Tianqi, et autres
Publié: (2024)

Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems
par: Kim, Juno, et autres
Publié: (2023)

Zeroth-Order Stochastic Mirror Descent Algorithms for Minimax Excess Risk Optimization
par: Gu, Zhihao, et autres
Publié: (2024)

Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
par: Li, Tianyou, et autres
Publié: (2023)

Negative Stepsizes Make Gradient-Descent-Ascent Converge
par: Shugart, Henry, et autres
Publié: (2025)

A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems
par: Zhang, Jiawei, et autres
Publié: (2020)

A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization
par: Lu, Songtao
Publié: (2022)

Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning
par: Li, Zihao, et autres
Publié: (2024)

Functional Central Limit Theorem for Stochastic Gradient Descent
par: Flamand, Kessang, et autres
Publié: (2026)

The Sample Complexity of Gradient Descent in Stochastic Convex Optimization
par: Livni, Roi
Publié: (2024)

More Optimal Fractional-Order Stochastic Gradient Descent for Non-Convex Optimization Problems
par: Partohaghighi, Mohammad, et autres
Publié: (2025)

Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems
par: Partohaghighi, Mohammad, et autres
Publié: (2025)

Stochastic Adaptive Gradient Descent Without Descent
par: Aujol, Jean-François, et autres
Publié: (2025)

Stochastic Semi-Gradient Descent for Learning Mean Field Games with Population-Aware Function Approximation
par: Zhang, Chenyu, et autres
Publié: (2024)

Rapid Overfitting of Multi-Pass Stochastic Gradient Descent in Stochastic Convex Optimization
par: Vansover-Hager, Shira, et autres
Publié: (2025)

Efficient Stochastic Approximation of Minimax Excess Risk Optimization
par: Zhang, Lijun, et autres
Publié: (2023)

Stochastic Gradient Descent with Adaptive Data
par: Che, Ethan, et autres
Publié: (2024)

Stochastic Gradient Descent with Strategic Querying
par: Jiang, Nanfei, et autres
Publié: (2025)

Adaptive Gradient Normalization and Independent Sampling for (Stochastic) Generalized-Smooth Optimization
par: Yang, Yufeng, et autres
Publié: (2024)

Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent
par: Ziyin, Liu, et autres
Publié: (2024)

Boosting Gradient Ascent for Continuous DR-submodular Maximization
par: Zhang, Qixin, et autres
Publié: (2024)

Momentum Does Not Reduce Stochastic Noise in Stochastic Gradient Descent
par: Sato, Naoki, et autres
Publié: (2024)

Derivatives of Stochastic Gradient Descent in parametric optimization
par: Iutzeler, Franck, et autres
Publié: (2024)

On the Convergence of Stochastic Gradient Descent with Perturbed Forward-Backward Passes
par: Kong, Boao, et autres
Publié: (2026)

Mirror Descent-Ascent for mean-field min-max problems
par: Lascu, Razvan-Andrei, et autres
Publié: (2024)

Faster Stochastic Algorithms for Minimax Optimization under Polyak--Łojasiewicz Conditions
par: Chen, Lesi, et autres
Publié: (2023)

An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization
par: Chen, Lesi, et autres
Publié: (2022)

Gradient Descent, Stochastic Optimization, and Other Tales
par: Lu, Jun
Publié: (2022)

Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent
par: Köhne, Frederik, et autres
Publié: (2023)