:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Durmus, Alain, Noble, Maxence, Pellerin, Thibaut
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Computation
Online Access:	https://arxiv.org/abs/2602.00641
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learned Reference-based Diffusion Sampling for multi-modal distributions
by: Noble, Maxence, et al.
Published: (2024)

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)

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

Diffusion-based Annealed Boltzmann Generators : benefits, pitfalls and hopes
by: Grenioux, Louis, 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)

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

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

On Sampling with Approximate Transport Maps
by: Grenioux, Louis, 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)

Geodesic slice sampling on Riemannian manifolds
by: Durmus, Alain, et al.
Published: (2023)

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

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

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

Fast Riemannian-manifold Hamiltonian Monte Carlo for hierarchical Gaussian-process models
by: Hayakawa, Takashi, et al.
Published: (2025)

Towards training digitally-tied analog blocks via hybrid gradient computation
by: Nest, Timothy, et al.
Published: (2024)

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

Heavy-Tailed Diffusion with Denoising Lévy Probabilistic Models
by: Shariatian, Dario, 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)

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

Efficient Sampling on Riemannian Manifolds via Langevin MCMC
by: Cheng, Xiang, et al.
Published: (2024)

Watermarking Makes Language Models Radioactive
by: Sander, Tom, et al.
Published: (2024)

Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation
by: Morisset, Lucas, et al.
Published: (2026)

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

Automated Journalistic Questions: A New Method for Extracting 5W1H in French
by: Verhaverbeke, Maxence, et al.
Published: (2025)

Emergent Riemannian geometry over learning discrete computations on continuous manifolds
by: Brandon, Julian, et al.
Published: (2025)

Exponential Convergence Guarantees for Iterative Markovian Fitting
by: Silveri, Marta Gentiloni, et al.
Published: (2025)

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

Non-Asymptotic Analysis of Data Augmentation for Precision Matrix Estimation
by: Morisset, Lucas, et al.
Published: (2025)

A training regime to learn unified representations from complementary breast imaging modalities
by: Sharma, Umang, et al.
Published: (2024)

Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction
by: Qu, Gang, et al.
Published: (2021)

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

Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians
by: Huix, Tom, et al.
Published: (2024)

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

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

KL Convergence Guarantees for Score diffusion models under minimal data assumptions
by: Conforti, Giovanni, et al.
Published: (2023)

Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization
by: Faldor, Maxence, et al.
Published: (2025)

Multi-modal Vision Pre-training for Medical Image Analysis
by: Rui, Shaohao, et al.
Published: (2024)

Riemannian Projection-free Online Learning
by: Hu, Zihao, et al.
Published: (2023)