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 consider stochastic optimization under distributional uncertainty, wherethe unknown distributional parameter is estimated from streaming data thatarrive sequentially over time. Moreover, data may depend on the decision of thetime when they are generated. For both decision-independent anddecision-dependent uncertainties, we propose an approach to jointly estimatethe distributional parameter via Bayesian posterior distribution and update thedecision by applying stochastic gradient descent on the Bayesian average of theobjective function. Our approach converges asymptotically over time andachieves the convergence rates of classical SGD in the decision-independentcase. We demonstrate the empirical performance of our approach on bothsynthetic test problems and a classical newsvendor problem.

Bayesian Stochastic Gradient Descent for Stochastic Optimization with Streaming Input Data