"Unsupervised learning"

Learning Topic Models - Provably and Efficiently

Max-margin learning with the Bayes Factor

We propose a new way to answer probabilistic queries that span multiple datapoints. We formalize reasoning about the similarity of different datapoints as the evaluation of the Bayes Factor within a hierarchical deep generative model that enforces a …

Semi-Amortized Variational Autoencoders

Amortized variational inference (AVI) replaces instance-specific local inference with a global inference network. While AVI has enabled efficient training of deep generative models such as variational autoencoders (VAE), recent empirical work …

Structured Inference Networks for Nonlinear State Space Models

Gaussian state space models have been used for decades as generative models of sequential data. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption. We introduce a unified algorithm to …

Clinical Tagging with Joint Probabilistic Models

We describe a method for parameter estimation in bipartite probabilistic graphical models for joint prediction of clinical conditions from the electronic medical record. The method does not rely on the availability of gold-standard labels, but rather …

Electronic Medical Record Phenotyping using the Anchor & Learn Framework

Electronic medical records (EMRs) hold a tremendous amount of information about patients that is relevant to determining the optimal approach to patient care. As medicine becomes increasingly precise, a patient’s electronic medical record phenotype …

Anchored Discrete Factor Analysis

We present a semi-supervised learning algorithm for learning discrete factor analysis models with arbitrary structure on the latent variables. Our algorithm assumes that every latent variable has an \"anchor\", an observed variable with only that …

Deep Kalman Filters

Kalman Filters are one of the most influential models of time-varying phenomena. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption in a variety of disciplines. Motivated by recent …

Unsupervised Learning of Disease Progression Models

Chronic diseases, such as Alzheimer's Disease, Diabetes, and Chronic Obstructive Pulmonary Disease, usually progress slowly over a long period of time, causing increasing burden to the patients, their families, and the healthcare system. A better …

Using Anchors to Estimate Clinical State without Labeled Data

We present a novel framework for learning to estimate and predict clinical state variables without labeled data. The resulting models can used for electronic phenotyping, triggering clinical decision support, and cohort selection. The framework …