51 citations · 116 across the 31 of their papers we have counts for
16 papers · 1 filter
Highly Data Parallelizable Estimation of the Sliced-Wasserstein Distance Using Cumulative Distribution Functions
Christophe Vauthier, Quentin Mérigot, Anna Korba
The Sliced Wasserstein (SW) distance has emerged as a computationally attractive alternative to the Wasserstein distance by leveraging one-dimensional optimal transport along rando…
Generalized Discrete Diffusion from Snapshots
Oussama Zekri, Théo Uscidda, Nicolas Boullé +1
We introduce Generalized Discrete Diffusion from Snapshots (GDDS), a unified framework for discrete diffusion modeling that supports arbitrary noising processes over large discrete…
Variational Inference with Mixtures of Isotropic Gaussians
Marguerite Petit-Talamon, Marc Lambert, Anna Korba
Variational inference (VI) is a popular approach in Bayesian inference, that looks for the best approximation of the posterior distribution within a parametric family, minimizing a…
Towards Understanding Gradient Dynamics of the Sliced-Wasserstein Distance via Critical Point Analysis
Christophe Vauthier, Anna Korba, Quentin Mérigot
In this paper, we investigate the properties of the Sliced Wasserstein Distance (SW) when employed as an objective functional. The SW metric has gained significant interest in the…
Constrained Sampling with Primal-Dual Langevin Monte Carlo
Luiz F. O. Chamon, Mohammad Reza Karimi, Anna Korba
This work considers the problem of sampling from a probability distribution known up to a normalization constant while satisfying a set of statistical constraints specified by the…
Provable Convergence and Limitations of Geometric Tempering for Langevin Dynamics
Omar Chehab, Anna Korba, Austin Stromme +1
Geometric tempering is a popular approach to sampling from challenging multi-modal probability distributions by instead sampling from a sequence of distributions which interpolate,…
(De)-regularized Maximum Mean Discrepancy Gradient Flow
Zonghao Chen, Aratrika Mustafi, Pierre Glaser +3
We introduce a (de)-regularization of the Maximum Mean Discrepancy (DrMMD) and its Wasserstein gradient flow. Existing gradient flows that transport samples from source distributio…
Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergence
Clémentine Chazal, Anna Korba, Francis Bach
In this paper, we study the statistical and geometrical properties of the Kullback-Leibler divergence with kernel covariance operators (KKL) introduced by Bach [2022]. Unlike the c…
A Practical Diffusion Path for Sampling
Omar Chehab, Anna Korba
Diffusion models are state-of-the-art methods in generative modeling when samples from a target probability distribution are available, and can be efficiently sampled, using score…
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians
Tom Huix, Anna Korba, Alain Durmus +1
Variational inference (VI) is a popular approach in Bayesian inference, that looks for the best approximation of the posterior distribution within a parametric family, minimizing a…
Sampling with Mollified Interaction Energy Descent
Lingxiao Li, Qiang Liu, Anna Korba +2
Sampling from a target measure whose density is only known up to a normalization constant is a fundamental problem in computational statistics and machine learning. In this paper,…
Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study
Tom Huix, Szymon Majewski, Alain Durmus +2
This paper studies the Variational Inference (VI) used for training Bayesian Neural Networks (BNN) in the overparameterized regime, i.e., when the number of neurons tends to infini…
Kernel Stein Discrepancy Descent
Anna Korba, Pierre-Cyril Aubin-Frankowski, Szymon Majewski +1
Among dissimilarities between probability distributions, the Kernel Stein Discrepancy (KSD) has received much interest recently. We investigate the properties of its Wasserstein gr…
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
Anna Korba, Adil Salim, Michael Arbel +2
We study the Stein Variational Gradient Descent (SVGD) algorithm, which optimises a set of particles to approximate a target probability distribution on $\mathbb{…
Maximum Mean Discrepancy Gradient Flow
Michael Arbel, Anna Korba, Adil Salim +1
We construct a Wasserstein gradient flow of the maximum mean discrepancy (MMD) and study its convergence properties. The MMD is an integral probability metric defined for a reprodu…
A Structured Prediction Approach for Label Ranking
Anna Korba, Alexandre Garcia, Florence d'Alché Buc
We propose to solve a label ranking problem as a structured output regression task. We adopt a least square surrogate loss approach that solves a supervised learning problem in two…