Narges Razavian

Learn More
The von Mises distribution is a continuous probability distribution on the circle used in directional statistics. In this paper, we introduce the undirected von Mises Graphical model and present an algorithm for parameter and structure learning using L 1 regularization. We show that the learning algorithm is both consistent and statistically efficient.(More)
We introduce three algorithms for learning generative models of molecular structures from molecular dynamics simulations. The first algorithm learns a Bayesian-optimal undirected probabilistic model over user-specified covariates (e.g., fluctuations, distances, angles, etc). L1 reg-ularization is used to ensure sparse models and thus reduce the risk of(More)
The von Mises model encodes a multivariate circular distribution as an undirected probabilistic graphical model. Presently, the only algorithm for performing inference in the model is Gibbs sampling, which becomes inefficient for large graphs. To address this issue, we introduce an Expectation Propagation based algorithm for performing inference in the von(More)
Early detection of Type 2 diabetes poses challenges to both the machine learning and medical communities. Current clinical practices focus on narrow patient-specific courses of action whereas electronic health records and insurance claims data give us the ability to generalize that knowledge across large sets of populations. Advances in population health(More)
The von Mises distribution is a continuous probability distribution on the circle used in directional statistics. In this paper, we introduce the undirected von Mises Graphical model and present an algorithm for structure learning using L 1 regularization. We show that the learning algorithm is both consistent and efficient. We also introduce a simple(More)
We introduce an algorithm for learning sparse, time-varying undirected probabilistic graphical models of Molecular Dynamics (MD) data. Our method computes a maximum a posteriori (MAP) estimate of the topology and parameters of the model (i.e., structure learning) using L1-regularization of the negative log-likelihood (aka 'Graphical Lasso') to ensure(More)
Disparate areas of machine learning have benefited from models that can take raw data with little preprocessing as input and learn rich representations of that raw data in order to perform well on a given prediction task. We evaluate this approach in healthcare by using longitudinal measurements of lab tests, one of the more raw signals of a patient's(More)
Early diagnosis of treatable diseases is essential for improving healthcare, and many diseases' onsets are predictable from annual lab tests and their temporal trends. We introduce a multi-resolution convolutional neural network for early detection of multiple diseases from irregularly measured sparse lab values. Our novel architecture takes as input both(More)
The von Mises distribution is a continuous probability distribution on the circle used in directional statistics. In this paper, we introduce the undirected von Mises Graphical model and present an algorithm for parameter and structure learning using L 1 regularization. We show that the learning algorithm is both consistent and statistically efficient.(More)