{"total":55,"rows":[{"id":"2205.01445","title":"High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation","created":"2022-05-03","cited_by_count":150,"area":null},{"id":"2209.14863","title":"Neural Networks Efficiently Learn Low-Dimensional Representations with SGD","created":"2022-09-29","cited_by_count":63,"area":null},{"id":"2212.07469","title":"Learning threshold neurons via the \"edge of stability\"","created":"2022-12-14","cited_by_count":51,"area":null},{"id":"2402.01258","title":"Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape","created":"2024-02-02","cited_by_count":46,"area":null},{"id":"2309.03843","title":"Gradient-Based Feature Learning under Structured Data","created":"2023-09-07","cited_by_count":32,"area":null},{"id":"2205.07146","title":"Trajectory Inference via Mean-field Langevin in Path Space","created":"2022-05-14","cited_by_count":30,"area":null},{"id":"2212.03050","title":"Uniform-in-time propagation of chaos for mean field Langevin dynamics","created":"2022-12-06","cited_by_count":23,"area":null},{"id":"2306.07221","title":"Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction","created":"2023-06-12","cited_by_count":20,"area":null},{"id":"2303.13429","title":"Interacting Particle Langevin Algorithm for Maximum Marginal Likelihood Estimation","created":"2023-03-23","cited_by_count":17,"area":null},{"id":"2408.07254","title":"Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics","created":"2024-08-14","cited_by_count":15,"area":null},{"id":"2212.08791","title":"Two-Scale Gradient Descent Ascent Dynamics Finds Mixed Nash Equilibria of Continuous Games: A Mean-Field Perspective","created":"2022-12-17","cited_by_count":13,"area":null},{"id":"2403.14917","title":"Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective","created":"2024-03-22","cited_by_count":13,"area":null},{"id":"2406.07475","title":"Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior","created":"2024-06-11","cited_by_count":13,"area":null},{"id":"2312.07335","title":"Momentum Particle Maximum Likelihood","created":"2023-12-12","cited_by_count":12,"area":null},{"id":"2405.15767","title":"Improved Particle Approximation Error for Mean Field Neural Networks","created":"2024-05-24","cited_by_count":12,"area":null},{"id":"2312.01127","title":"Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems","created":"2023-12-02","cited_by_count":11,"area":null},{"id":"2303.11844","title":"Doubly Regularized Entropic Wasserstein Barycenters","created":"2023-03-21","cited_by_count":10,"area":null},{"id":"2402.07355","title":"Sampling from the Mean-Field Stationary Distribution","created":"2024-02-12","cited_by_count":7,"area":null},{"id":"2512.08176","title":"Worst-case generation via minimax optimization in Wasserstein space","created":"2025-12-09","cited_by_count":7,"area":null},{"id":"2505.15270","title":"Scaling Diffusion Transformers Efficiently via $\\mu$P","created":"2025-05-21","cited_by_count":6,"area":"diffusion"},{"id":"2406.03944","title":"Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples","created":"2024-06-06","cited_by_count":5,"area":null},{"id":"2410.16449","title":"Robust Feature Learning for Multi-Index Models in High Dimensions","created":"2024-10-21","cited_by_count":5,"area":null},{"id":"2502.02954","title":"Direct Distributional Optimization for Provable Alignment of Diffusion Models","created":"2025-02-05","cited_by_count":5,"area":"diffusion"},{"id":"2502.05784","title":"Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble","created":"2025-02-09","cited_by_count":5,"area":null},{"id":"2504.18208","title":"Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime","created":"2025-04-25","cited_by_count":5,"area":null},{"id":"2303.02957","title":"Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems","created":"2023-03-06","cited_by_count":4,"area":null},{"id":"2411.15067","title":"Linear convergence of proximal descent schemes on the Wasserstein space","created":"2024-11-22","cited_by_count":4,"area":null},{"id":"2503.09565","title":"Global Convergence and Rich Feature Learning in $L$-Layer Infinite-Width Neural Networks under $\\mu$P Parametrization","created":"2025-03-12","cited_by_count":4,"area":null},{"id":"2205.09860","title":"Mean-Field Analysis of Two-Layer Neural Networks: Global Optimality with Linear Convergence Rates","created":"2022-05-19","cited_by_count":3,"area":null},{"id":"2210.06819","title":"Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence","created":"2022-10-13","cited_by_count":3,"area":null},{"id":"2210.16286","title":"A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks","created":"2022-10-28","cited_by_count":3,"area":null},{"id":"2505.02621","title":"Mirror Mean-Field Langevin Dynamics","created":"2025-05-05","cited_by_count":3,"area":null},{"id":"2605.22010","title":"Uniform-in-Time Weak Propagation-of-Chaos in Shallow Neural Networks","created":"2026-05-21","cited_by_count":3,"area":null},{"id":"2302.05938","title":"Mean Field Optimization Problem Regularized by Fisher Information","created":"2023-02-12","cited_by_count":2,"area":null},{"id":"2312.16360","title":"Mean-field underdamped Langevin dynamics and its spacetime discretization","created":"2023-12-26","cited_by_count":2,"area":null},{"id":"2402.08106","title":"Mirror Descent-Ascent for mean-field min-max problems","created":"2024-02-12","cited_by_count":2,"area":null},{"id":"2402.15984","title":"A unified Fourier slice method to derive ridgelet transform for a variety of depth-2 neural networks","created":"2024-02-25","cited_by_count":2,"area":null},{"id":"2405.19995","title":"Symmetries in Overparametrized Neural Networks: A Mean-Field View","created":"2024-05-30","cited_by_count":2,"area":null},{"id":"2501.14993","title":"Convergence Analysis of the Wasserstein Proximal Algorithm beyond Geodesic Convexity","created":"2025-01-25","cited_by_count":2,"area":null},{"id":"2505.07642","title":"Convergence of Time-Averaged Mean Field Gradient Descent Dynamics for Continuous Multi-Player Zero-Sum Games","created":"2025-05-12","cited_by_count":2,"area":null},{"id":"2505.22760","title":"Non-convex entropic mean-field optimization via Best Response flow","created":"2025-05-28","cited_by_count":2,"area":null},{"id":"2509.16974","title":"Hessian-guided Perturbed Wasserstein Gradient Flows for Escaping Saddle Points","created":"2025-09-21","cited_by_count":2,"area":null},{"id":"2605.22622","title":"A note on convergence of Wasserstein policy optimization","created":"2026-05-21","cited_by_count":2,"area":"deeprl"},{"id":"2412.20824","title":"Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret","created":"2024-12-30","cited_by_count":1,"area":null},{"id":"2503.13115","title":"Beyond Propagation of Chaos: A Stochastic Algorithm for Mean Field Optimization","created":"2025-03-17","cited_by_count":1,"area":null},{"id":"2605.17963","title":"Wasserstein Saddle-Free Newton: Saddle Escape and Local Spectral Convergence","created":"2026-05-18","cited_by_count":1,"area":null},{"id":"2605.28589","title":"Thinned Mean Field Langevin Dynamics","created":"2026-05-27","cited_by_count":1,"area":null},{"id":"2607.03871","title":"A Gradient Flow Perspective on Minimum MMD Estimation","created":"2026-07-04","cited_by_count":1,"area":null},{"id":"2506.12203","title":"Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamics","created":"2025-06-13","cited_by_count":0,"area":null},{"id":"2508.04883","title":"Gaussian mixture layers for neural networks","created":"2025-08-06","cited_by_count":0,"area":null},{"id":"2512.21075","title":"Feature Learning Dynamics in Infinite-Depth Neural Networks","created":"2025-12-24","cited_by_count":0,"area":null},{"id":"2602.03999","title":"Functional Stochastic Localization","created":"2026-02-03","cited_by_count":0,"area":null},{"id":"2602.15094","title":"On propagation of chaos for the Fisher-Rao gradient flow in entropic mean-field optimization","created":"2026-02-16","cited_by_count":0,"area":null},{"id":"2609.39091","title":"Steepest Guidance: A Practical and Principled Approach to Inference-Time Alignment of Flow and Diffusion-based Models","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.20014","title":"On the Convergence of Semi-Gradient TD: A Mean Field Perspective","created":"2026-10-14","cited_by_count":0,"area":"deeprl"}],"next_cursor":null}