Alain Durmus

papers

306

total citations

papers (12)

Statistical and Topological Properties of Sliced Probability Divergences

NEURIPS 2020arXiv

100

citations

FedPop: A Bayesian Approach for Personalised Federated Learning

NEURIPS 2022arXiv

citations

Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections

NEURIPS 2021arXiv

citations

Quantitative Propagation of Chaos for SGD in Wide Neural Networks

NEURIPS 2020arXiv

citations

Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize

NEURIPS 2021arXiv

citations

Local-Global MCMC kernels: the best of both worlds

NEURIPS 2022arXiv

citations

Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters

NEURIPS 2023arXiv

citations

Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo

NEURIPS 2023arXiv

citations

Alain Durmus

papers (12)

Statistical and Topological Properties of Sliced Probability Divergences

FedPop: A Bayesian Approach for Personalised Federated Learning

Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections

Quantitative Propagation of Chaos for SGD in Wide Neural Networks

Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize

Local-Global MCMC kernels: the best of both worlds

Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters

Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo

NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform

Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent

Non-Asymptotic Analysis Of Data Augmentation For Precision Matrix Estimation

Exponential Convergence Guarantees for Iterative Markovian Fitting

papers (12)

Statistical and Topological Properties of Sliced Probability Divergences

FedPop: A Bayesian Approach for Personalised Federated Learning

Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections

Quantitative Propagation of Chaos for SGD in Wide Neural Networks

Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize

Local-Global MCMC kernels: the best of both worlds

Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters

Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo

NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform

Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent

Non-Asymptotic Analysis Of Data Augmentation For Precision Matrix Estimation

Exponential Convergence Guarantees for Iterative Markovian Fitting