Accelerated Mixing Time of Randomized Hamiltonian Monte Carlo
We show the Randomized Hamiltonian Monte Carlo (RHMC) algorithm has accelerated mixing time guarantees for sampling from log-concave probability distributions. RHMC proceeds by repeatedly simulating the continuous-time Hamiltonian dynamics for some random integration times, and resetting the velocity to be an independent Gaussian random variable between each simulation. We show that when the target distribution is log-concave and satisfies an $α$-Talagrand inequality (for example, if the target distribution is $α$-strongly log-concave), if we use a random integration time from either the trian
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- LinkedLinked via arxiv author · 85%Siddharth Mitra →
“Accelerated Mixing Time of Randomized Hamiltonian Monte Carlo”
- LinkedLinked via arxiv author · 85%Vishwak Srinivasan →
“Accelerated Mixing Time of Randomized Hamiltonian Monte Carlo”
- LinkedLinked via arxiv author · 85%Xiuyuan Wang →
“Accelerated Mixing Time of Randomized Hamiltonian Monte Carlo”
- LinkedLinked via arxiv author · 85%Andre Wibisono →
“Accelerated Mixing Time of Randomized Hamiltonian Monte Carlo”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”