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We discuss different modeling choices and a selected number of important algorithms.

We show how a subfamily of our new methods adapts to this setting, proving new upper and lower bounds on the log partition function and deriving a family of sequential samplers for the Gibbs distribution. Abstract: Sparse approximations for Gaussian process models provide a suite of methods that enable these models to be deployed in large data regime and enable analytic intractabilities to be sidestepped.

Finally, we balance the discussion by showing how the simpler analytical form of the Gumbel trick enables additional theoretical results. However, the field lacks a principled method to handle streaming data in which the posterior distribution over function values and the hyperparameters are updated in an online fashion.

The approach is to train a neural network to predict properties of the program that generated the outputs from the inputs.

We use the neural network's predictions to augment search techniques from the programming languages community, including enumerative search and an SMT-based solver.

Alpha-divergences are alternative divergences to VI’s KL objective, which are able to avoid VI’s uncertainty underestimation.

But these are hard to use in practice: existing techniques can only use Gaussian approximating distributions, and require existing models to be changed radically, thus are of limited use for practitioners.

The proposed framework is experimentally validated using synthetic and real-world datasets. Although elegant, the application of GPs is limited by computational and analytical intractabilities that arise when data are sufficiently numerous or when employing non-Gaussian models.