English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We propose a fast stochastic Hamilton Monte Carlo (HMC) method, for samplingfrom a smooth and strongly log-concave distribution. At the core of ourproposed method is a variance reduction technique inspired by the recentadvance in stochastic optimization. We show that, to achieve $\epsilon$accuracy in 2-Wasserstein distance, our algorithm achieves $\tildeO(n+\kappa^{2}d^{1/2}/\epsilon+\kappa^{4/3}d^{1/3}n^{2/3}/\epsilon^{2/3})$gradient complexity (i.e., number of component gradient evaluations), whichoutperforms the state-of-the-art HMC and stochastic gradient HMC methods in awide regime. We also extend our algorithm for sampling from smooth and generallog-concave distributions, and prove the corresponding gradient complexity aswell. Experiments on both synthetic and real data demonstrate the superiorperformance of our algorithm.

Stochastic Variance-Reduced Hamilton Monte Carlo Methods