← Authors

Alain Durmus

35 papers · 2016–2024 · 5 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (8) 🐝 Cross-Pollinator (10) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) 🌍 Conference Polyglot (5) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (16) 🧭 Keyword Pioneer 🏠 Conference Loyalist (21) πŸ”¬ Deep Specialist (15) 🀝 Dynamic Duo (17) πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (153) ⚑ Prolific Year (8) πŸ’Ž Century Club (35) πŸ”₯ Unstoppable (9)

Conferences

NIPS (21) AISTATS (6) COLT (4) ICML (3) JMLR (1)

Top co-authors

Eric Moulines (17) Umut Simsekli (8) Valentin De Bortoli (4) Vincent Plassier (3) Maxime Vono (3) Kimia Nadjahi (3) Alexey Naumov (3) Sergey Samsonov (3) Arnaud Doucet (3) Roland Badeau (3)

Research topics

Differential Privacy (1)

Keywords

markov chain monte carlo (14) bayesian inference (9) optimal transport (4) stochastic gradient descent (4) wasserstein distance (4) generative model (3) markov chain (3) langevin dynamics (3) langevin monte carlo (3) federated learning (3) stochastic approximation (3) stochastic gradient (2) stochastic process (2) hamiltonian monte carlo (2) generative modeling (2) stationary distribution (2) random projection (2) importance sampling (2) posterior sampling (2) variance reduction (2)

Papers

Unravelling in Collaborative Learning NIPS 2024
Divide-and-Conquer Posterior Sampling for Denoising Diffusion priors NIPS 2024
Theoretical guarantees in KL for Diffusion Flow Matching NIPS 2024
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training AISTATS 2024
Stochastic Approximation with Biased MCMC for Expectation Maximization AISTATS 2024
Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality NIPS 2024
Watermarking Makes Language Models Radioactive NIPS 2024
Piecewise deterministic generative models NIPS 2024
Shape analysis for time series NIPS 2024
Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo AISTATS 2023
Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo NIPS 2023
Tree-Based Diffusion SchrΓΆdinger Bridge with Applications to Wasserstein Barycenters NIPS 2023
Non-asymptotic convergence bounds for Sinkhorn iterates and their gradients: a coupling approach. COLT 2023
Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent NIPS 2023
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms AISTATS 2023
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning AISTATS 2022
FedPop: A Bayesian Approach for Personalised Federated Learning NIPS 2022
Local-Global MCMC kernels: the best of both worlds NIPS 2022
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs ICML 2021
Convergence rates and approximation results for SGD and its continuous-time counterpart COLT 2021
Monte Carlo Variational Auto-Encoders ICML 2021
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections NIPS 2021
NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform NIPS 2021
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize NIPS 2021
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size AISTATS 2021
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning COLT 2021
Quantitative Propagation of Chaos for SGD in Wide Neural Networks NIPS 2020
Statistical and Topological Properties of Sliced Probability Divergences NIPS 2020
Analysis of Langevin Monte Carlo via Convex Optimization JMLR 2019
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance NIPS 2019
Copula-like Variational Inference NIPS 2019
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions ICML 2019
The promises and pitfalls of Stochastic Gradient Langevin Dynamics NIPS 2018
Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo COLT 2017
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo NIPS 2016