Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence
Fuente:
arXiv
Saved in:
| Main Authors: | Han, Yinbin, Razaviyayn, Meisam, Xu, Renyuan |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulators
by: Han, Yinbin, et al.
Published: (2023)
by: Han, Yinbin, et al.
Published: (2023)
Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization
by: Han, Yinbin, et al.
Published: (2024)
by: Han, Yinbin, et al.
Published: (2024)
Private Stochastic Optimization With Large Worst-Case Lipschitz Parameter
by: Lowy, Andrew, et al.
Published: (2022)
by: Lowy, Andrew, et al.
Published: (2022)
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
by: Lowy, Andrew, et al.
Published: (2021)
by: Lowy, Andrew, et al.
Published: (2021)
Less is More: Convergence Benefits of Fewer Data Weight Updates over Longer Horizon
by: Das, Rudrajit, et al.
Published: (2026)
by: Das, Rudrajit, et al.
Published: (2026)
Optimal Differentially Private Model Training with Public Data
by: Lowy, Andrew, et al.
Published: (2023)
by: Lowy, Andrew, et al.
Published: (2023)
On the Inherent Privacy of Zeroth Order Projected Gradient Descent
by: Gupta, Devansh, et al.
Published: (2025)
by: Gupta, Devansh, et al.
Published: (2025)
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control
by: Domingo-Enrich, Carles, et al.
Published: (2024)
by: Domingo-Enrich, Carles, et al.
Published: (2024)
Adaptive Partitioning and Learning for Stochastic Control of Diffusion Processes
by: Jin, Hanqing, et al.
Published: (2025)
by: Jin, Hanqing, et al.
Published: (2025)
Fast Policy Learning for Linear Quadratic Control with Entropy Regularization
by: Guo, Xin, et al.
Published: (2023)
by: Guo, Xin, et al.
Published: (2023)
Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms
by: Mohammadi, Hesameddin, et al.
Published: (2022)
by: Mohammadi, Hesameddin, et al.
Published: (2022)
Controlling the Flow: Stability and Convergence for Stochastic Gradient Descent with Decaying Regularization
by: Kassing, Sebastian, et al.
Published: (2025)
by: Kassing, Sebastian, et al.
Published: (2025)
Scalable Bi-causal Optimal Transport via KL Relaxation and Policy Gradients
by: Cao, Haoyang, et al.
Published: (2026)
by: Cao, Haoyang, et al.
Published: (2026)
Risk-sensitive Markov Decision Process and Learning under General Utility Functions
by: Wu, Zhengqi, et al.
Published: (2023)
by: Wu, Zhengqi, et al.
Published: (2023)
Adjoint Matching through the Lens of the Stochastic Maximum Principle in Optimal Control
by: Domingo-Enrich, Carles, et al.
Published: (2026)
by: Domingo-Enrich, Carles, et al.
Published: (2026)
Nearly Optimal Linear Convergence of Stochastic Primal-Dual Methods for Linear Programming
by: Lu, Haihao, et al.
Published: (2021)
by: Lu, Haihao, et al.
Published: (2021)
MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control
by: Zhu, Yuchen, et al.
Published: (2025)
by: Zhu, Yuchen, et al.
Published: (2025)
Nonconvex Stochastic Optimization under Heavy-Tailed Noises: Optimal Convergence without Gradient Clipping
by: Liu, Zijian, et al.
Published: (2024)
by: Liu, Zijian, et al.
Published: (2024)
Optimal Local Convergence Rates of Stochastic First-Order Methods under Local $α$-PL
by: Masiha, Saeed, et al.
Published: (2024)
by: Masiha, Saeed, et al.
Published: (2024)
A Taxonomy of Loss Functions for Stochastic Optimal Control
by: Domingo-Enrich, Carles
Published: (2024)
by: Domingo-Enrich, Carles
Published: (2024)
Finite-Time Decoupled Convergence in Nonlinear Two-Time-Scale Stochastic Approximation
by: Han, Yuze, et al.
Published: (2024)
by: Han, Yuze, et al.
Published: (2024)
Stochastic Optimal Control Matching
by: Domingo-Enrich, Carles, et al.
Published: (2023)
by: Domingo-Enrich, Carles, et al.
Published: (2023)
Improving Generalization and Convergence by Enhancing Implicit Regularization
by: Wang, Mingze, et al.
Published: (2024)
by: Wang, Mingze, et al.
Published: (2024)
An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control
by: Hua, Mengjian, et al.
Published: (2024)
by: Hua, Mengjian, et al.
Published: (2024)
On Policy Stochasticity in Mutual Information Optimal Control of Linear Systems
by: Enami, Shoju, et al.
Published: (2025)
by: Enami, Shoju, et al.
Published: (2025)
Solving Sparse \& High-Dimensional-Output Regression via Compression
by: Li, Renyuan, et al.
Published: (2024)
by: Li, Renyuan, et al.
Published: (2024)
A Provably Convergent Plug-and-Play Framework for Stochastic Bilevel Optimization
by: Chu, Tianshu, et al.
Published: (2025)
by: Chu, Tianshu, et al.
Published: (2025)
Gradient Regularized Newton Boosting Trees with Global Convergence
by: Zozoulenko, Nikita, et al.
Published: (2026)
by: Zozoulenko, Nikita, et al.
Published: (2026)
A Schrödinger Eigenfunction Method for Long-Horizon Stochastic Optimal Control
by: Claeys, Louis, et al.
Published: (2026)
by: Claeys, Louis, et al.
Published: (2026)
Convergence Rate in Nonlinear Two-Time-Scale Stochastic Approximation with State (Time)-Dependence
by: Chen, Zixi, et al.
Published: (2025)
by: Chen, Zixi, et al.
Published: (2025)
Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach
by: Fernando, Heshan, et al.
Published: (2022)
by: Fernando, Heshan, et al.
Published: (2022)
Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces
by: Hoischen, Nicolas, et al.
Published: (2024)
by: Hoischen, Nicolas, et al.
Published: (2024)
MMD-Regularized Unbalanced Optimal Transport
by: Manupriya, Piyushi, et al.
Published: (2020)
by: Manupriya, Piyushi, et al.
Published: (2020)
Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees
by: Deng, Yuyang, et al.
Published: (2024)
by: Deng, Yuyang, et al.
Published: (2024)
On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions
by: Hong, Yusu, et al.
Published: (2024)
by: Hong, Yusu, et al.
Published: (2024)
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
by: Grazzi, Riccardo, et al.
Published: (2024)
by: Grazzi, Riccardo, et al.
Published: (2024)
Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives
by: Oikonomidis, Konstantinos, et al.
Published: (2026)
by: Oikonomidis, Konstantinos, et al.
Published: (2026)
Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
by: Li, Tianyou, et al.
Published: (2023)
by: Li, Tianyou, et al.
Published: (2023)
Implicit Bias and Convergence of Matrix Stochastic Mirror Descent
by: Akhtiamov, Danil, et al.
Published: (2026)
by: Akhtiamov, Danil, et al.
Published: (2026)
Convergence Rate of the Last Iterate of Stochastic Proximal Algorithms
by: Vaidyan, Kevin Kurian Thomas, et al.
Published: (2026)
by: Vaidyan, Kevin Kurian Thomas, et al.
Published: (2026)
Similar Items
-
Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulators
by: Han, Yinbin, et al.
Published: (2023) -
Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization
by: Han, Yinbin, et al.
Published: (2024) -
Private Stochastic Optimization With Large Worst-Case Lipschitz Parameter
by: Lowy, Andrew, et al.
Published: (2022) -
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
by: Lowy, Andrew, et al.
Published: (2021) -
Less is More: Convergence Benefits of Fewer Data Weight Updates over Longer Horizon
by: Das, Rudrajit, et al.
Published: (2026)