We describe a simple, low-level approach for embedding probabilistic programming in a deep learning ecosystem.

AIS is asymptotically optimal, i.e., learning the sampling policy has no cost asymptotically.

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).

Neural Information Processing Systems http://nips.cc/

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.