6 papers
Accelerated Mixing Time of Randomized Hamiltonian Monte Carlo
Siddharth Mitra, Vishwak Srinivasan, Xiuyuan Wang +1
We show the Randomized Hamiltonian Monte Carlo (RHMC) algorithm has accelerated mixing time guarantees for sampling from log-concave probability distributions. RHMC proceeds by rep…
Accelerated Convex Optimization via Hamiltonian Dynamics with Deterministic Integration Time
Xiuyuan Wang, Vishwak Srinivasan, Qiang Fu +3
We develop Hamiltonian dynamics-based algorithms for smooth convex optimization that achieve accelerated rates of convergence. By exploiting contraction of averaged Hamiltonian flo…
Tail-Sensitive KL and Rényi Convergence of Unadjusted Hamiltonian Monte Carlo via One-Shot Couplings
Nawaf Bou-Rabee, Siddharth Mitra, Andre Wibisono
Hamiltonian Monte Carlo (HMC) algorithms are among the most widely used sampling methods in high dimensional settings, yet their convergence properties are poorly understood in div…
On the Convergence of Min-Max Langevin Dynamics and Algorithm
Yang Cai, Siddharth Mitra, Xiuyuan Wang +1
We study zero-sum games in the space of probability distributions over the Euclidean space with entropy regularization, in the setting when the interaction function…
Fast Convergence of -Divergence Along the Unadjusted Langevin Algorithm and Proximal Sampler
Siddharth Mitra, Andre Wibisono
We study the mixing time of two popular discrete-time Markov chains in continuous space, the Unadjusted Langevin Algorithm and the Proximal Sampler, which are discretizations of th…
Characterizing Dependence of Samples along the Langevin Dynamics and Algorithms via Contraction of -Mutual Information
Jiaming Liang, Siddharth Mitra, Andre Wibisono
The mixing time of a Markov chain determines how fast the iterates of the Markov chain converge to the stationary distribution; however, it does not control the dependencies betwee…