2 papers
math.OC2024
Losing momentum in continuous-time stochastic optimisation
Kexin Jin, Jonas Latz, Chenguang Liu +1
The training of modern machine learning models often consists in solving high-dimensional non-convex optimisation problems that are subject to large-scale data. In this context, mo…
stat.ML2024
Subsampling Error in Stochastic Gradient Langevin Diffusions
Kexin Jin, Chenguang Liu, Jonas Latz
The Stochastic Gradient Langevin Dynamics (SGLD) are popularly used to approximate Bayesian posterior distributions in statistical learning procedures with large-scale data. As opp…