5 papers
Analysis of kinetic Langevin Monte Carlo under the stochastic exponential Euler discretization from underdamped all the way to overdamped
Kyurae Kim, Samuel Gruffaz, Ji Won Park +1
Simulating the kinetic Langevin dynamics is a popular approach for sampling from distributions, where only their unnormalized densities are available. Various discretizations of th…
A Theoretical Comparison of No-U-Turn Sampler Variants: Necessary and Sufficient Convergence Conditions and Mixing Time Analysis under Gaussian Targets
Samuel Gruffaz, Kyurae Kim, Fares Guehtar +2
The No-U-Turn Sampler (NUTS) is the computational workhorse of modern Bayesian software libraries, yet its qualitative and quantitative convergence guarantees were established only…
Geodesic slice sampling on Riemannian manifolds
Alain Durmus, Samuel Gruffaz, Mareike Hasenpflug +1
We propose a theoretically justified and practically applicable slice sampling based Markov chain Monte Carlo (MCMC) method for approximate sampling from probability measures on Ri…
Personalized Convolutional Dictionary Learning of Physiological Time Series
Axel Roques, Samuel Gruffaz, Kyurae Kim +2
Human physiological signals tend to exhibit both global and local structures: the former are shared across a population, while the latter reflect inter-individual variability. For…
On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers
Alain Durmus, Samuel Gruffaz, Miika Kailas +2
There is substantial empirical evidence about the success of dynamic implementations of Hamiltonian Monte Carlo (HMC), such as the No U-Turn Sampler (NUTS), in many challenging inf…