On the efficiency of adaptive MCMC algorithms. - Christophe Andrieu; Yves F. Atchadé (VALUETOOLS, 2006)

A computational framework for empirical Bayes inference. - Yves F. Atchadé (Stat. Comput., 2011)

Towards optimal scaling of metropolis-coupled Markov chain Monte Carlo. - Yves F. Atchadé; Gareth O. Roberts; Jeffrey S. Rosenthal (Stat. Comput., 2011)

Modeling Choice Interdependence in a Social Network. - Jing Wang 104; Anocha Aribarg; Yves F. Atchadé (Mark. Sci., 2013)

Approximate Bayesian Computation for Exponential Random Graph Models for Large Social Networks. - Jing Wang 104; Yves F. Atchadé (Commun. Stat. Simul. Comput., 2014)

On the convergence rates of some adaptive Markov chain Monte Carlo algorithms. - Yves F. Atchadé; Yizao Wang (J. Appl. Probab., 2015)

On Perturbed Proximal Gradient Algorithms. - Yves F. Atchadé; Gersende Fort; Eric Moulines (J. Mach. Learn. Res., 2017)

Stochastic Fista Algorithms: So Fast ? - Gersende Fort; Laurent Risser; Yves F. Atchadé; Eric Moulines (SSP, 2018)

Change-Point Computation for Large Graphical Models: A Scalable Algorithm for Gaussian Graphical Models with Change-Points. - Leland Bybee; Yves F. Atchadé (J. Mach. Learn. Res., 2018)

Quasi-Bayesian estimation of large Gaussian graphical models. - Yves F. Atchadé (J. Multivar. Anal., 2019)

On Bayesian sparse canonical correlation analysis via Rayleigh quotient framework. - Qiuyun Zhu; Yves F. Atchadé (CoRR, 2020)

Sequential change-point detection in high-dimensional Gaussian graphical models. - Hossein Keshavarz; George Michailidis; Yves F. Atchadé (J. Mach. Learn. Res., 2020)

Markov chain Monte Carlo algorithms with sequential proposals. - Joonha Park; Yves F. Atchadé (Stat. Comput., 2020)

Approximate Spectral Gaps for Markov Chain Mixing Times in High Dimensions. - Yves F. Atchadé (SIAM J. Math. Data Sci., 2021)

Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models. - Alexander Lin; Bahareh Tolooshams; Yves F. Atchadé; Demba E. Ba (ICML, 2023)

A statistical perspective on algorithm unrolling models for inverse problems. - Yves F. Atchadé; Xinru Liu; Qiuyun Zhu (CoRR, 2023)

On the estimation rate of Bayesian PINN for inverse problems. - Yi Sun; Debarghya Mukherjee; Yves F. Atchadé (CoRR, 2024)

Data-driven rainfall prediction at a regional scale: a case study with Ghana. - Indrajit Kalita; Lucia Vilallonga; Yves F. Atchadé (CoRR, 2024)