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8 papers · 1 filter

stat.ML2026

Theoretical Guarantees for SMC-Guided Diffusion Sampling

Stanislas Strasman, Gabriel Victorino Cardoso, Sylvain Le Corff +2

Post-hoc conditioning of pretrained diffusion models can be addressed using Sequential Monte Carlo (SMC) methods. By evolving an interacting particle system, SMC-guided diffusion s…

stat.ML2026

Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models

Stanislas Strasman, Sobihan Surendran, Sylvain Le Corff

Score-based Generative Models (SGMs) have achieved impressive performance in data generation across a wide range of applications. While the statistical properties of their sampling…

math.DG2026

Alexandrov spaces with non negative curvature and displacement convexity of the entropy tensor

Jordan Serres

On a smooth Riemannian manifold, Aishwarya, Rotem and Shenfeld characterised nonnegative sectional curvature as the matrix displacement convexity of an entropy tensor, the Lagrangi…

stat.ME2026

A duration-augmented binary Markov chain for rainfall occurrence with long dry spells

Antoine Doizé, Denis Allard, Philippe Naveau +1

Simulating realistic wet and dry spells is central in weather generators and climate-impact studies. While finite-order Markov chains are standard, they often fail to reproduce per…

stat.ML2026

Increasing Missingness to Reduce Bias: Richardson-SGD with Missing Data

Ferdinand Genans, Erwan Scornet

Stochastic gradient methods are central to modern large-scale learning, but their use with incomplete covariates remains delicate since imputation schemes generally introduce syste…

math.ST2026

High-Probability Minimax Adaptive Estimation in Besov Spaces via Online-to-Batch

Paul Liautaud, Pierre Gaillard, Olivier Wintenberger

We study nonparametric regression over Besov spaces from noisy observations under sub-exponential noise, aiming to achieve minimax-optimal guarantees on the integrated squared erro…

math.OC2026

Fast and Large-Scale Unbalanced Optimal Transport via its Semi-Dual and Adaptive Gradient Methods

Ferdinand Genans

Unbalanced Optimal Transport (UOT) has emerged as a robust relaxation of standard Optimal Transport, particularly effective for handling outliers and mass variations. However, scal…

stat.ML2026

Geometry-Aware Optimal Transport: Fast Intrinsic Dimension and Wasserstein Distance Estimation

Ferdinand Genans, Olivier Wintenberger

Solving large scale Optimal Transport (OT) in machine learning typically relies on sampling measures to obtain a tractable discrete problem. While the discrete solver's accuracy is…