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Pyro: Deep Universal Probabilistic Programming

Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning framework to accommodate complex or model-specific algorithmic behavior.
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Conditional Similarity Networks

This work proposes Conditional Similarity Networks (CSNs) that learn embeddings differentiated into semantically distinct subspaces that capture the different notions of similarities.
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Bayesian representation learning with oracle constraints

It is shown how implicit triplet information can provide rich information to learn representations that outperform previous metric learning approaches as well as generative models without this side-information in a variety of predictive tasks.
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RiboDiff: detecting changes of mRNA translation efficiency from ribosome footprints

A statistical framework and analysis tool, RiboDiff, to detect genes with changes in translation efficiency across experimental treatments and performs a statistical test for differential translation efficiency using both mRNA abundance and ribosome occupancy.
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Likelihood-free inference with emulator networks

This work presents a new ABC method which uses probabilistic neural emulator networks to learn synthetic likelihoods on simulated data -- both local emulators which approximate the likelihood for specific observed data, as well as global ones which are applicable to a range of data.
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Adversarial Message Passing For Graphical Models

This work treats GANs as a basis for likelihood-free inference in generative models and generalizes them to Bayesian posterior inference over factor graphs, finding that Bayesian inference on structured models can be performed only with sampling and discrimination when using nonparametric variational families, without access to explicit distributions.
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Generalized Hidden Parameter MDPs Transferable Model-based RL in a Handful of Trials

The GHP-MDP augments model-based RL with latent variables that capture these hidden parameters, facilitating transfer across tasks and explores a variant of the model that incorporates explicit latent structure mirroring the causal factors of variation across tasks (for instance: agent properties, environmental factors, and goals).
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Probabilistic Meta-Representations Of Neural Networks

This work considers a richer prior distribution in which units in the network are represented by latent variables, and the weights between units are drawn conditionally on the values of the collection of those variables.
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Variational Auto-Regressive Gaussian Processes for Continual Learning

Experiments on standard continual learning benchmarks demonstrate the ability of VAR-GPs to perform well at new tasks without compromising performance on old ones, yielding competitive results to state-of-the-art methods.
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An Empirical Analysis of Topic Modeling for Mining Cancer Clinical Notes

Using a variety of techniques including Topic Modeling, Principal Component Analysis and Bi-clustering, we explore electronic patient records in the form of unstructured clinical notes and genetic…
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