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

This work examines the convergence of stochastic gradient-based optimizationalgorithms that use early stopping based on a validation function. The form ofearly stopping we consider is that optimization terminates when the norm of thegradient of a validation function falls below a threshold. We derive conditionsthat guarantee this stopping rule is well-defined, and provide bounds on theexpected number of iterations and gradient evaluations needed to meet thiscriterion. The guarantee accounts for the distance between the training andvalidation sets, measured with the Wasserstein distance. We develop theapproach in the general setting of a first-order optimization algorithm, withpossibly biased update directions subject to a geometric drift condition. Wethen derive bounds on the expected running time for early stopping variants ofseveral algorithms, including stochastic gradient descent (SGD), decentralizedSGD (DSGD), and the stochastic variance reduced gradient (SVRG) algorithm.Finally, we consider the generalization properties of the iterate returned byearly stopping.

Bounding the expected run-time of nonconvex optimization with early stopping