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Omer Weissbrod

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3 papers

2 author rows

JMLR Journal 2019 Journal Article

Maximum Likelihood for Gaussian Process Classification and Generalized Linear Mixed Models under Case-Control Sampling

Omer Weissbrod
Shachar Kaufman
David Golan
Saharon Rosset

Modern data sets in various domains often include units that were sampled non-randomly from the population and have a latent correlation structure. Here we investigate a common form of this setting, where every unit is associated with a latent variable, all latent variables are correlated, and the probability of sampling a unit depends on its response. Such settings often arise in case-control studies, where the sampled units are correlated due to spatial proximity, family relations, or other sources of relatedness. Maximum likelihood estimation in such settings is challenging from both a computational and statistical perspective, necessitating approximations that take the sampling scheme into account. We propose a family of approximate likelihood approaches which combine composite likelihood and expectation propagation. We demonstrate the efficacy of our solutions via extensive simulations. We utilize them to investigate the genetic architecture of several complex disorders collected in case-control genetic association studies, where hundreds of thousands of genetic variants are measured for every individual, and the underlying disease liabilities of individuals are correlated due to genetic similarity. Our work is the first to provide a tractable likelihood-based solution for case-control data with complex dependency structures. [abs] [ pdf ][ bib ] &copy JMLR 2019. ( edit, beta )

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NeurIPS Conference 2014 Conference Paper

Approximating Hierarchical MV-sets for Hierarchical Clustering

Assaf Glazer
Omer Weissbrod
Michael Lindenbaum
Shaul Markovitch

The goal of hierarchical clustering is to construct a cluster tree, which can be viewed as the modal structure of a density. For this purpose, we use a convex optimization program that can efficiently estimate a family of hierarchical dense sets in high-dimensional distributions. We further extend existing graph-based methods to approximate the cluster tree of a distribution. By avoiding direct density estimation, our method is able to handle high-dimensional data more efficiently than existing density-based approaches. We present empirical results that demonstrate the superiority of our method over existing ones.

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UAI Conference 2014 Conference Paper

Structured Proportional Jump Processes

Tal El-Hay
Omer Weissbrod
Elad Eban
Maurizio Zazzi
Francesca Incardona

Learning the association between observed variables and future trajectories of continuoustime stochastic processes is a fundamental task in dynamic modeling. Often the dynamics are non-homogeneous and involve a large number of interacting components. We introduce a conditional probabilistic model that captures such dynamics, while maintaining scalability and providing an explicit way to express the interrelation between the system components. The principal idea is a factorization of the model into two distinct elements: one depends only on time and the other depends on the system configuration. We developed a learning procedure, given either full or point observations, and tested it on simulated data. We applied the proposed modeling scheme to study large cohorts of diabetes and HIV patients, and demonstrate that the factorization helps shed light on the dynamics of these diseases.

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