Synthesizing time series data is pivotal in modern society, aiding effective decision-making and ensuring privacy preservation in various scenarios.

Learning disentangled and distributed representation ofgenerativefactors oftheworld isbelieved tobenefit compositional generalization, because those invariant features canbereused assymbols to build exponentially larger amounts of objects with higher complexity [1, 2, 3, 4].

This procedure means that the model parametersθt, which we use to evaluate the model on the current test documentD(t), already encodes knowledge from previous test documents 18 D(1),D(2),,D(t 1), in addition to the knowledge learnt from the training set. "COVID-19" in late-2019), which is then stored in the model parameters, and reuse such information for better prediction of subsequent test documents. This means that the same model parametersθ1 (i.e. England international Steven Gerrard was cleared by a court in Liverpoolofaffray.

WederivePAC-Bayesian learning guarantees forheavy-tailed losses, andobtain a novel optimal Gibbs posterior which enjoys finite-sample excess risk bounds atlogarithmic confidence. Ourcoretechnique itselfmakesuseofPAC-Bayesian inequalities in order to derive a robust risk estimator, which by design is easy to compute.