Cover Image

Learned Reference-based Diffusion Sampling for multi-modal distributions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Noble, Maxence, Grenioux, Louis, Gabrié, Marylou, Durmus, Alain Oliviero
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computation
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Stochastic Localization via Iterative Posterior Sampling
by: Grenioux, Louis, et al.
Published: (2024)

Improving the evaluation of samplers on multi-modal targets
by: Grenioux, Louis, et al.
Published: (2025)

On Sampling with Approximate Transport Maps
by: Grenioux, Louis, et al.
Published: (2023)

Balanced Training of Energy-Based Models with Adaptive Flow Sampling
by: Grenioux, Louis, et al.
Published: (2023)

Sampling from multi-modal distributions on Riemannian manifolds with training-free stochastic interpolants
by: Durmus, Alain, et al.
Published: (2026)

Diffusion-based Annealed Boltzmann Generators : benefits, pitfalls and hopes
by: Grenioux, Louis, et al.
Published: (2026)

Riemannian Stochastic Interpolants for Amorphous Particle Systems
by: Grenioux, Louis, et al.
Published: (2025)

Annealing in variational inference mitigates mode collapse: A theoretical study on Gaussian mixtures
by: Fogliani, Luigi, et al.
Published: (2026)

Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy
by: Molina-Taborda, Ana, et al.
Published: (2024)

A theoretical perspective on mode collapse in variational inference
by: Soletskyi, Roman, et al.
Published: (2024)

A Diffusive Classification Loss for Learning Energy-based Generative Models
by: OuYang, RuiKang, et al.
Published: (2026)

Stochastic Approximation with Biased MCMC for Expectation Maximization
by: Gruffaz, Samuel, et al.
Published: (2024)

The critical slowing down in diffusion models
by: Del Bono, Luca Maria, et al.
Published: (2026)

Analysis of kinetic Langevin Monte Carlo under the stochastic exponential Euler discretization from underdamped all the way to overdamped
by: Kim, Kyurae, et al.
Published: (2025)

Non-asymptotic convergence bounds for Sinkhorn iterates and their gradients: a coupling approach
by: Greco, Giacomo, et al.
Published: (2023)

Efficient Zero-Shot Inpainting with Decoupled Diffusion Guidance
by: Moufad, Badr, et al.
Published: (2025)

Briding Diffusion Posterior Sampling and Monte Carlo methods: a survey
by: Janati, Yazid, et al.
Published: (2025)

Sampling metastable systems using collective variables and Jarzynski-Crooks paths
by: Schönle, Christoph, et al.
Published: (2024)

Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors
by: Janati, Yazid, et al.
Published: (2024)

When Test-Time Guidance Is Enough: Fast Image and Video Editing with Diffusion Guidance
by: Ghorbel, Ahmed, et al.
Published: (2026)

Sampling from multi-modal distributions with polynomial query complexity in fixed dimension via reverse diffusion
by: Vacher, Adrien, et al.
Published: (2024)

VITS : Variational Inference Thompson Sampling for contextual bandits
by: Clavier, Pierre, et al.
Published: (2023)

Fast, faithful and photorealistic diffusion-based image super-resolution with enhanced Flow Map models
by: Noble, Maxence, et al.
Published: (2026)

Heavy-Tailed Diffusion with Denoising Lévy Probabilistic Models
by: Shariatian, Dario, et al.
Published: (2024)

Variational Diffusion Posterior Sampling with Midpoint Guidance
by: Moufad, Badr, et al.
Published: (2024)

Differential privacy guarantees of Markov chain Monte Carlo algorithms
by: Bertazzi, Andrea, et al.
Published: (2025)

Iterative Tilting for Diffusion Fine-Tuning
by: Pachebat, Jean, et al.
Published: (2025)

Theoretical guarantees in KL for Diffusion Flow Matching
by: Silveri, Marta Gentiloni, et al.
Published: (2024)

Personalized Convolutional Dictionary Learning of Physiological Time Series
by: Roques, Axel, et al.
Published: (2025)

Latent-Augmented Discrete Diffusion Models
by: Shariatian, Dario, et al.
Published: (2025)

Categorical Reparameterization with Denoising Diffusion models
by: Gourevitch, Samson, et al.
Published: (2026)

Algorithm- and Data-Dependent Generalization Bounds for Diffusion Models
by: Dupuis, Benjamin, et al.
Published: (2025)

Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality
by: Scheid, Antoine, et al.
Published: (2024)

Dreaming in Code for Curriculum Learning in Open-Ended Worlds
by: Mitsides, Konstantinos, et al.
Published: (2026)

Differentially Private Representation Learning via Image Captioning
by: Sander, Tom, et al.
Published: (2024)

Diffusive Gibbs Sampling
by: Chen, Wenlin, et al.
Published: (2024)

Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training
by: Sander, Tom, et al.
Published: (2024)

On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers
by: Durmus, Alain, et al.
Published: (2023)

Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs
by: Farashah, Alireza Dehghanpour, et al.
Published: (2026)

Coarse Grained Molecular Dynamics with Normalizing Flows
by: Tamagnone, Samuel, et al.
Published: (2024)