Alain Durmus
35 papers · 2016–2024 · 5 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+11 more ↓ Show less ↑
π§ Keyword Pioneer π Renaissance Researcher (5) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (16) π£ Hot Topic Early Bird
π
Interdisciplinary Bridge
πΊοΈ
Taxonomy Completionist
(16)
π§
Keyword Pioneer
π
Conference Loyalist
(21)
π¬
Deep Specialist
(15)
π€
Dynamic Duo
(17)
π
Keyword Champion
(2)
ποΈ
Keyword Collector
(153)
β‘
Prolific Year
(8)
π
Century Club
(35)
π₯
Unstoppable
(9)
Conferences
NIPS (21)
AISTATS (6)
COLT (4)
ICML (3)
JMLR (1)
Top co-authors
Research topics
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