Many stochastic processes in the physical and biological sciences can be...
In previous work, we introduced a method for determining convergence rat...
We provide a framework to prove convergence rates for discretizations of...
Hamiltonian Monte Carlo (HMC) algorithms which combine numerical
approxi...
We propose multirate training of neural networks: partitioning neural ne...
We employ constraints to control the parameter space of deep neural netw...
We study the design and implementation of numerical methods to solve the...
We propose a method for efficiently incorporating constraints into a
sto...
Stochastic Gradient Langevin Dynamics, the "unadjusted Langevin algorith...
Adaptive Langevin dynamics is a method for sampling the Boltzmann-Gibbs
...
Adaptive Langevin dynamics is a method for sampling the Boltzmann-Gibbs
...
We describe a TensorFlow-based library for posterior sampling and explor...
We investigate the theoretical foundations of the simulated tempering me...
We describe parallel Markov chain Monte Carlo methods that propagate a
c...
Monte Carlo sampling for Bayesian posterior inference is a common approa...