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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present a new accelerated stochastic second-order method that is robust toboth gradient and Hessian inexactness, which occurs typically in machinelearning. We establish theoretical lower bounds and prove that our algorithmachieves optimal convergence in both gradient and Hessian inexactness in thiskey setting. We further introduce a tensor generalization for stochastichigher-order derivatives. When the oracles are non-stochastic, the proposedtensor algorithm matches the global convergence of Nesterov Accelerated Tensormethod. Both algorithms allow for approximate solutions of their auxiliarysubproblems with verifiable conditions on the accuracy of the solution.

Advancing the lower bounds: An accelerated, stochastic, second-order method with optimal adaptation to inexactness