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mean-field analysis

12 papers

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neural network (3) stochastic gradient descent (3) representation learning (3) wasserstein gradient flow (2) gradient flow (2) gradient descent (2) neural network optimization (2) global convergence (2) mean field theory (2) learning theory (2)

Papers

A Mean-Field Analysis of Neural Stochastic Gradient Descent-Ascent for Functional Minimax Optimization JMLR 2026 FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization JMLR 2024 Global Convergence in Training Large-Scale Transformers NeurIPS 2024 Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input NeurIPS 2024 Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time NeurIPS 2023 Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks JMLR 2022 Overparameterization of Deep ResNet: Zero Loss and Mean-field Analysis JMLR 2022 Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime NeurIPS 2022 Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic NeurIPS 2021 A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth ICML 2020 A mean-field analysis of two-player zero-sum games NeurIPS 2020 Sinkhorn Barycenter via Functional Gradient Descent NeurIPS 2020