Copertina

Learning Paths for Dynamic Measure Transport: A Control Perspective

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Maurais, Aimee, Hosseini, Bamdad, Marzouk, Youssef
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Computation
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Sampling in Unit Time with Kernel Fisher-Rao Flow
di: Maurais, Aimee, et al.
Pubblicazione: (2024)

Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices
di: Maurais, Aimee, et al.
Pubblicazione: (2023)

Diffusion map particle systems for generative modeling
di: Li, Fengyi, et al.
Pubblicazione: (2023)

An Eulerian Perspective on Straight-Line Sampling
di: Tsimpos, Panos, et al.
Pubblicazione: (2025)

Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation
di: Chen, Yifan, et al.
Pubblicazione: (2024)

Learning local neighborhoods of non-Gaussian graphical models: A measure transport approach
di: Liaw, Sarah, et al.
Pubblicazione: (2025)

Fast filtering of non-Gaussian models using Amortized Optimal Transport Maps
di: Al-Jarrah, Mohammad, et al.
Pubblicazione: (2025)

Optimal Scheduling of Dynamic Transport
di: Tsimpos, Panos, et al.
Pubblicazione: (2025)

Conditional Optimal Transport on Function Spaces
di: Hosseini, Bamdad, et al.
Pubblicazione: (2023)

An Approximation Theory Framework for Measure-Transport Sampling Algorithms
di: Baptista, Ricardo, et al.
Pubblicazione: (2023)

Expected information gain estimation via density approximations: Sample allocation and dimension reduction
di: Li, Fengyi, et al.
Pubblicazione: (2024)

To discretize continually: Mean shift interacting particle systems for Bayesian inference
di: Belhadji, Ayoub, et al.
Pubblicazione: (2026)

Data Curation for Machine Learning Interatomic Potentials by Determinantal Point Processes
di: Zou, Joanna, et al.
Pubblicazione: (2026)

Nonlinear Filtering with Brenier Optimal Transport Maps
di: Al-Jarrah, Mohammad, et al.
Pubblicazione: (2023)

Neural Triangular Transport Maps: A New Approach Towards Sampling in Lattice QCD
di: Bryutkin, Andrey, et al.
Pubblicazione: (2025)

On the representation and learning of monotone triangular transport maps
di: Baptista, Ricardo, et al.
Pubblicazione: (2020)

Diffeomorphic Measure Matching with Kernels for Generative Modeling
di: Pandey, Biraj, et al.
Pubblicazione: (2024)

Conditional Sampling via Wasserstein Autoencoders and Triangular Transport
di: Al-Jarrah, Mohammad, et al.
Pubblicazione: (2026)

Can Bayesian Neural Networks Make Confident Predictions?
di: Fisher, Katharine, et al.
Pubblicazione: (2025)

Error Analysis of Bayesian Inverse Problems with Generative Priors
di: Hosseini, Bamdad, et al.
Pubblicazione: (2026)

Error Analysis of Triangular Optimal Transport Maps for Filtering
di: Al-Jarrah, Mohammad, et al.
Pubblicazione: (2025)

Canonical Bayesian Linear System Identification
di: Bryutkin, Andrey, et al.
Pubblicazione: (2025)

Dimension reduction via score ratio matching
di: Baptista, Ricardo, et al.
Pubblicazione: (2024)

Physics-Informed Neural Networks for Joint Source and Parameter Estimation in Advection-Diffusion Equations
di: Anague, Brenda, et al.
Pubblicazione: (2025)

One-Shot Generative Flows: Existence and Obstructions
di: Tsimpos, Panos, et al.
Pubblicazione: (2026)

Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities
di: Li, Fengyi, et al.
Pubblicazione: (2024)

Preserving linear invariants in ensemble filtering methods
di: Provost, Mathieu Le, et al.
Pubblicazione: (2024)

Bandit Learning in Matching Markets: Utilitarian and Rawlsian Perspectives
di: Hosseini, Hadi, et al.
Pubblicazione: (2024)

On the design of scalable, high-precision spherical-radial Fourier features
di: Belhadji, Ayoub, et al.
Pubblicazione: (2024)

Transport map unadjusted Langevin algorithms: learning and discretizing perturbed samplers
di: Zhang, Benjamin J., et al.
Pubblicazione: (2023)

Machine-Learned Sampling of Conditioned Path Measures
di: Jiang, Qijia, et al.
Pubblicazione: (2025)

Weighted quantization using MMD: From mean field to mean shift via gradient flows
di: Belhadji, Ayoub, et al.
Pubblicazione: (2025)

Multitask methods for predicting molecular properties from heterogeneous data
di: Fisher, Katharine, et al.
Pubblicazione: (2024)

Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities
di: Li, Matthew T. C., et al.
Pubblicazione: (2024)

Generative Modeling through Koopman Spectral Analysis: An Operator-Theoretic Perspective
di: Xu, Yuanchao, et al.
Pubblicazione: (2025)

Distribution learning via neural differential equations: minimal energy regularization and approximation theory
di: Marzouk, Youssef, et al.
Pubblicazione: (2025)

Score Operator Newton transport
di: Chandramoorthy, Nisha, et al.
Pubblicazione: (2023)

Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis
di: Jalalian, Yasamin, et al.
Pubblicazione: (2025)

A low-rank ensemble Kalman filter for elliptic observations
di: Provost, Mathieu Le, et al.
Pubblicazione: (2022)

LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport
di: Cao, Lianghao, et al.
Pubblicazione: (2024)