Cover Image

Kernel-based potential mean-field games with unbiased random Fourier $U$-statistics

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Nakano, Yumiharu
Format:	Preprint
Published:	2026
Subjects:	Optimization and Control Machine Learning Numerical Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Continuum-marginal optimal transport: a mesh-free kernel method
by: Nakano, Yumiharu
Published: (2026)

A kernel-based method for Schrödinger bridges
by: Nakano, Yumiharu
Published: (2023)

Convergence of differentiable non-monotone schemes for fully nonlinear parabolic equations
by: Nakano, Yumiharu
Published: (2018)

Solving Monge problem by Hilbert space embeddings of probability measures
by: Saito, Takafumi, et al.
Published: (2024)

Convergence of two-timescale gradient descent ascent dynamics: finite-dimensional and mean-field perspectives
by: An, Jing, et al.
Published: (2025)

Non-exchangeable evolutionary and mean field games and their applications
by: Yoshioka, H., et al.
Published: (2025)

Travel-time tomography from mean field game dynamics
by: Xu, Longqiang, et al.
Published: (2026)

Computational analysis on a linkage between generalized logit dynamic and discounted mean field game
by: Yoshioka, Hidekazu
Published: (2024)

Nonlinear Assimilation via Score-based Sequential Langevin Sampling
by: Ding, Zhao, et al.
Published: (2024)

A distributed semismooth Newton based augmented Lagrangian method for distributed optimization
by: Ma, Qihao, et al.
Published: (2026)

Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models
by: Tomasetto, Matteo, et al.
Published: (2024)

Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models
by: Tomasetto, Matteo, et al.
Published: (2024)

Transformers Can Implement Preconditioned Richardson Iteration for In-Context Gaussian Kernel Regression
by: Yan, Mingsong, et al.
Published: (2026)

Learning epidemic trajectories through Kernel Operator Learning: from modelling to optimal control
by: Ziarelli, Giovanni, et al.
Published: (2024)

Generalized pair-wise logit dynamic and its connection to a mean field game: theoretical and computational investigations focusing on resource management
by: Yoshioka, Hidekazu, et al.
Published: (2024)

Dynamic Proximal Gradient Algorithms for Schatten-$p$ Quasi-Norm Regularized Problems
by: Shen, Weiping, et al.
Published: (2026)

Why is Normalization Preferred? A Worst-Case Complexity Theory for Stochastically Preconditioned SGD under Heavy-Tailed Noise
by: Fang, Yuchen, et al.
Published: (2026)

Natural Riemannian gradient for learning functional tensor networks
by: Klug, Nikolas, et al.
Published: (2026)

State-Dependent Lyapunov Method for Rank-1 Matrix Factorization
by: Moon, Jaehong
Published: (2026)

Primal-Dual Methods for Nonsmooth Nonconvex Optimization with Orthogonality Constraints
by: Zhu, Linglingzhi, et al.
Published: (2026)

Solving the Offline and Online Min-Max Problem of Non-smooth Submodular-Concave Functions: A Zeroth-Order Approach
by: Farzin, Amir Ali, et al.
Published: (2026)

WinQ: Accelerating Quantization-Aware Training of Language Models Around Saddle Points
by: Li, Dongyue, et al.
Published: (2026)

Last-Iterate Convergence of Randomized Kaczmarz and SGD with Greedy Step Size
by: Dereziński, Michał, et al.
Published: (2026)

OptEMA: Adaptive Exponential Moving Average for Stochastic Optimization with Zero-Noise Optimality
by: Yuan, Ganzhao
Published: (2026)

Nonlinear model reduction for transport-dominated problems
by: Hesthaven, Jan S., et al.
Published: (2026)

Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization
by: Gong, Xindi, et al.
Published: (2026)

Adaptive Lipschitz-Free Conditional Gradient Methods for Stochastic Composite Nonconvex Optimization
by: Yuan, Ganzhao
Published: (2026)

Beyond Muon: MUD (MomentUm Decorrelation) for Faster Transformer Training
by: Southworth, Ben S., et al.
Published: (2026)

A Trust-Region Interior-Point Stochastic Sequential Quadratic Programming Method
by: Fang, Yuchen, et al.
Published: (2026)

Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton
by: Niu, Chengmei, et al.
Published: (2025)

Scalable Acceleration for Classification-Based Derivative-Free Optimization
by: Han, Tianyi, et al.
Published: (2023)

Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses
by: Dereich, Steffen, et al.
Published: (2024)

On the numerical reliability of nonsmooth autodiff: a MaxPool case study
by: Boustany, Ryan
Published: (2024)

Error Feedback Can Accurately Compress Preconditioners
by: Modoranu, Ionut-Vlad, et al.
Published: (2023)

Linear convergence of forward-backward accelerated algorithms without knowledge of the modulus of strong convexity
by: Li, Bowen, et al.
Published: (2023)

A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks
by: Mishra, Neel, et al.
Published: (2024)

Subhomogeneous Deep Equilibrium Models
by: Sittoni, Pietro, et al.
Published: (2024)

Solving Elliptic Optimal Control Problems via Neural Networks and Optimality System
by: Dai, Yongcheng, et al.
Published: (2023)

Stochastic Langevin Differential Inclusions with Applications to Machine Learning
by: Difonzo, Fabio V., et al.
Published: (2022)

Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance
by: Stollenwerk, Alexander, et al.
Published: (2024)