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

This work provides test error bounds for iterative fixed point methods onlinear predictors -- specifically, stochastic and batch mirror descent (MD),and stochastic temporal difference learning (TD) -- with two corecontributions: (a) a single proof technique which gives high probabilityguarantees despite the absence of projections, regularization, or anyequivalents, even when optima have large or infinite norm, forquadratically-bounded losses (e.g., providing unified treatment of squared andlogistic losses); (b) locally-adapted rates which depend not on global problemstructure (such as condition numbers and maximum margins), but rather onproperties of low norm predictors which may suffer some small excess testerror. The proof technique is an elementary and versatile coupling argument,and is demonstrated here in the following settings: stochastic MD underrealizability; stochastic MD for general Markov data; batch MD for general IIDdata; stochastic MD on heavy-tailed data (still without projections);stochastic TD on Markov chains (all prior stochastic TD bounds are inexpectation).

Stochastic linear optimization never overfits with quadratically-bounded losses on general data