Cover Image

An optimal control perspective on diffusion-based generative modeling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Berner, Julius, Richter, Lorenz, Ullrich, Karen
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Improved sampling via learned diffusions
by: Richter, Lorenz, et al.
Published: (2023)

Dynamical Measure Transport and Neural PDE Solvers for Sampling
by: Sun, Jingtong, et al.
Published: (2024)

A unified perspective on fine-tuning and sampling with diffusion and flow models
by: Domingo-Enrich, Carles, et al.
Published: (2026)

Evaluating the design space of diffusion-based generative models
by: Wang, Yuqing, et al.
Published: (2024)

On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates
by: Bruno, Stefano, et al.
Published: (2023)

Terminally constrained flow-based generative models from an optimal control perspective
by: Gao, Weiguo, et al.
Published: (2026)

A new perspective on low-rank optimization
by: Bertsimas, Dimitris, et al.
Published: (2021)

Fine-tuning of diffusion models via stochastic control: entropy regularization and beyond
by: Tang, Wenpin, et al.
Published: (2024)

Aligned Multi Objective Optimization
by: Efroni, Yonathan, et al.
Published: (2025)

Reinforcement Learning with Random Time Horizons
by: Borrell, Enric Ribera, et al.
Published: (2025)

Regret Analysis: a control perspective
by: Gibson, Travis E., et al.
Published: (2025)

Worst-case generation via minimax optimization in Wasserstein space
by: Cheng, Xiuyuan, et al.
Published: (2025)

A learning-based approach to stochastic optimal control under reach-avoid constraint
by: Ni, Tingting, et al.
Published: (2024)

A minimax optimal control approach for robust neural ODEs
by: Cipriani, Cristina, et al.
Published: (2023)

Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization
by: Mayfrank, Daniel, et al.
Published: (2024)

Smart energy management: process structure-based hybrid neural networks for optimal scheduling and economic predictive control in integrated systems
by: Wu, Long, et al.
Published: (2024)

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

Convergence of empirical subgradients for optimal transport-based objectives
by: Le, Tam
Published: (2026)

Learning based convex approximation for constrained parametric optimization
by: Liu, Kang, et al.
Published: (2025)

Adam with model exponential moving average is effective for nonconvex optimization
by: Ahn, Kwangjun, et al.
Published: (2024)

Joint Parameter and State-Space Bayesian Optimization: Using Process Expertise to Accelerate Manufacturing Optimization
by: Kiroriwal, Saksham, et al.
Published: (2026)

Solving a class of stochastic optimal control problems by physics-informed neural networks
by: Jiao, Zhe, et al.
Published: (2024)

Time-optimal neural feedback control of nilpotent systems as a binary classification problem
by: Bicego, Sara, et al.
Published: (2025)

Surrogate-based optimization of system architectures subject to hidden constraints
by: Bussemaker, Jasper, et al.
Published: (2025)

SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
by: Islam, H M Mohaimanul, et al.
Published: (2024)

Safe Bayesian optimization across noise models via scenario programming
by: Tokmak, Abdullah, et al.
Published: (2025)

What price to pay? Auto-tuning a building MPC controller for optimal economic cost
by: Yu, Jiarui, et al.
Published: (2025)

A successive approximation method in functional spaces for hierarchical optimal control problems and its application to learning
by: Befekadu, Getachew K.
Published: (2024)

A learning-based mathematical programming formulation for the automatic configuration of optimization solvers
by: Iommazzo, Gabriele, et al.
Published: (2024)

On improving generalization in a class of learning problems with the method of small parameters for weakly-controlled optimal gradient systems
by: Befekadu, Getachew K.
Published: (2024)

Efficient model predictive control for nonlinear systems modelled by deep neural networks
by: Lan, Jianglin
Published: (2024)

Safe learning-based control via function-based uncertainty quantification
by: Tokmak, Abdullah, et al.
Published: (2026)

Learning to optimize: A tutorial for continuous and mixed-integer optimization
by: Chen, Xiaohan, et al.
Published: (2024)

A Convex-optimization-based Layer-wise Post-training Pruner for Large Language Models
by: Zhao, Pengxiang, et al.
Published: (2024)

Infinity-norm-based Input-to-State-Stable Long Short-Term Memory networks: a thermal systems perspective
by: De Carli, Stefano, et al.
Published: (2025)

A simple uniformly optimal method without line search for convex optimization
by: Li, Tianjiao, et al.
Published: (2023)

Convergence of flow-based generative models via proximal gradient descent in Wasserstein space
by: Cheng, Xiuyuan, et al.
Published: (2023)

BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification
by: Lu, Yen-An, et al.
Published: (2024)

Optimal and Order-optimal Gated Priority-based Greedy Policies for Two-layer Multi-item Order Fulfillment
by: Chen, Xi, et al.
Published: (2026)

Unifying back-propagation and forward-forward algorithms through model predictive control
by: Ren, Lianhai, et al.
Published: (2024)