4 papers
Practical and Scalable Hamiltonian Monte Carlo Without the Metropolis Test
Jakob Robnik, Reuben Cohn-Gordon, Uroš Seljak
Hamiltonian Monte Carlo and underdamped Langevin Monte Carlo are leading methods for sampling from high-dimensional distributions with differentiable densities. Both rely on numeri…
Counterdiabatic Hamiltonian Monte Carlo
Reuben Cohn-Gordon, Uroš Seljak, Dries Sels
Hamiltonian Monte Carlo (HMC) is a state of the art method for sampling from distributions with differentiable densities, but can converge slowly when applied to challenging multim…
Machine-Learned Sampling of Conditioned Path Measures
Qijia Jiang, Reuben Cohn-Gordon
We propose algorithms for sampling from posterior path measures under a general prior process. This leverages ideas from (1) controlled equilibrium dyn…
Metropolis Adjusted Microcanonical Hamiltonian Monte Carlo
Jakob Robnik, Reuben Cohn-Gordon, Uroš Seljak
Sampling from high dimensional distributions is a computational bottleneck in many scientific applications. Hamiltonian Monte Carlo (HMC), and in particular the No-U-Turn Sampler (…