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 study the generalization error of randomized learning algorithms --focusing on stochastic gradient descent (SGD) -- using a novel combination ofPAC-Bayes and algorithmic stability. Importantly, our generalization boundshold for all posterior distributions on an algorithm's random hyperparameters,including distributions that depend on the training data. This inspires anadaptive sampling algorithm for SGD that optimizes the posterior at runtime. Weanalyze this algorithm in the context of our generalization bounds and evaluateit on a benchmark dataset. Our experiments demonstrate that adaptive samplingcan reduce empirical risk faster than uniform sampling while also improvingout-of-sample accuracy.

A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent