We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations.

Canonically, the variational principle suggests to prefer an expressive inference model so that the variational approximation is accurate.

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We also provide a general convergence analysis to support our empirical findings. Although our analysis is similar to CPI's, it has a key difference: as long as MBOC succeeds, we can provide a larger policy improvement than CPI at each iteration.

In this work, we aim to warm-start BO in the context of hyperparameter optimization (HPO).

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We also provide an algorithm for sampling CPDAGs efficiently from their posterior given the learned tree. That is, not from the full posterior, but from a reduced space of CPDAGs encoded in the learned tree.

Unlike classical graphical models, QGMs represent uncertainty with density matrices in complex Hilbert spaces.

Apart from modeling the time to event, in the presence of competing risks, it is also important to model the event type, or under which risk the event is likely to occur first. Though one can censor subjects with an occurrence of the event under a competing risk other than the risk of special interest, so that every survival model that can handle censoring is able to model competing risks, it is problematic to violate the principle of non-informative censoring [18, 19].

D 2 N, define [ D ]: = { 1,...,D } . The symbol & indicates greater than up to a universal constant.