Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization. - Umut Simsekli; Çagatay Yildiz; Thanh Huy Nguyen 001; A. Taylan Cemgil; Gaël Richard (ICML, 2018)

Efficient Bayesian Model Selection in PARAFAC via Stochastic Thermodynamic Integration. - Thanh Huy Nguyen 001; Umut Simsekli; Gaël Richard; Ali Taylan Cemgil (IEEE Signal Process. Lett., 2018)

Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization. - Thanh Huy Nguyen 001; Umut Simsekli; Gaël Richard (ICML, 2019)

First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise. - Thanh Huy Nguyen 001; Umut Simsekli; Mert Gürbüzbalaban; Gaël Richard (NeurIPS, 2019)

On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks. - Umut Simsekli; Mert Gürbüzbalaban; Thanh Huy Nguyen 001; Gaël Richard; Levent Sagun (CoRR, 2019)

Heavy-tailed nature of stochastic gradient descent in deep learning : theoretical and empirical analysis. (Nature à queue lourde de l'algorithme du gradient stochastique en apprentissage profond : analyse théorique et empirique). - Thanh Huy Nguyen 001 (2021)