Cover Image

Neural Optimal Transport with Lagrangian Costs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Pooladian, Aram-Alexandre, Domingo-Enrich, Carles, Chen, Ricky T. Q., Amos, Brandon
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Stochastic Optimal Control Matching
by: Domingo-Enrich, Carles, et al.
Published: (2023)

Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control
by: Domingo-Enrich, Carles, et al.
Published: (2024)

A Taxonomy of Loss Functions for Stochastic Optimal Control
by: Domingo-Enrich, Carles
Published: (2024)

Wasserstein Flow Matching: Generative modeling over families of distributions
by: Haviv, Doron, et al.
Published: (2024)

Adjoint Matching through the Lens of the Stochastic Maximum Principle in Optimal Control
by: Domingo-Enrich, Carles, et al.
Published: (2026)

Progressive Entropic Optimal Transport Solvers
by: Kassraie, Parnian, et al.
Published: (2024)

Plug-in estimation of Schrödinger bridges
by: Pooladian, Aram-Alexandre, et al.
Published: (2024)

Optimal transport map estimation in general function spaces
by: Divol, Vincent, et al.
Published: (2022)

Entropic estimation of optimal transport maps
by: Pooladian, Aram-Alexandre, et al.
Published: (2021)

Trust Region Constrained Measure Transport in Path Space for Stochastic Optimal Control and Inference
by: Blessing, Denis, et al.
Published: (2025)

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

Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space
by: Jiang, Yiheng, et al.
Published: (2023)

Learning Elastic Costs to Shape Monge Displacements
by: Klein, Michal, et al.
Published: (2023)

Compress Then Test: Powerful Kernel Testing in Near-linear Time
by: Domingo-Enrich, Carles, et al.
Published: (2023)

Blind denoising diffusion models and the blessings of dimensionality
by: Kadkhodaie, Zahra, et al.
Published: (2026)

Stability of the Kim--Milman flow map
by: Chewi, Sinho, et al.
Published: (2025)

Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
by: Havens, Aaron, et al.
Published: (2025)

TaskMet: Task-Driven Metric Learning for Model Learning
by: Bansal, Dishank, et al.
Published: (2023)

Variational inference via radial transport
by: Ghafourpour, Luca, et al.
Published: (2026)

Rare Event Analysis via Stochastic Optimal Control
by: Du, Yuanqi, et al.
Published: (2026)

Theory and computation for structured variational inference
by: Sheng, Shunan, et al.
Published: (2025)

Cheap Permutation Testing
by: Domingo-Enrich, Carles, et al.
Published: (2025)

Cross-Domain Imitation Learning via Optimal Transport
by: Fickinger, Arnaud, et al.
Published: (2021)

Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps
by: Baptista, Ricardo, et al.
Published: (2024)

Conditioning Diffusions Using Malliavin Calculus
by: Pidstrigach, Jakiw, et al.
Published: (2025)

Reinforce Adjoint Matching: Scaling RL Post-Training of Diffusion and Flow-Matching Models
by: Bergmeister, Andreas, et al.
Published: (2026)

Neural Optimal Transport with General Cost Functionals
by: Asadulaev, Arip, et al.
Published: (2022)

Value Gradient Guidance for Flow Matching Alignment
by: Liu, Zhen, et al.
Published: (2025)

On amortizing convex conjugates for optimal transport
by: Amos, Brandon
Published: (2022)

No Compute Left Behind: Rethinking Reasoning and Sampling with Masked Diffusion Models
by: Horvitz, Zachary, et al.
Published: (2025)

Any-Order Flexible Length Masked Diffusion
by: Kim, Jaeyeon, et al.
Published: (2025)

Tutorial on amortized optimization
by: Amos, Brandon
Published: (2022)

Simulation-Free Differential Dynamics through Neural Conservation Laws
by: Hua, Mengjian, et al.
Published: (2025)

Matching Features, Not Tokens: Energy-Based Fine-Tuning of Language Models
by: Jelassi, Samy, et al.
Published: (2026)

Flow Matching on General Geometries
by: Chen, Ricky T. Q., et al.
Published: (2023)

Test-time scaling of diffusions with flow maps
by: Sabour, Amirmojtaba, et al.
Published: (2025)

Hyperparameter Trajectory Inference with Conditional Lagrangian Optimal Transport
by: Amad, Harry, et al.
Published: (2026)

Reject, Resample, Repeat: Understanding Parallel Reasoning in Language Model Inference
by: Golowich, Noah, et al.
Published: (2026)

FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions
by: Sriram, Anuroop, et al.
Published: (2024)

Free energy Estimation on Any State Space
by: He, Jiajun, et al.
Published: (2026)