Armeen Taeb

Assistant Professor, University of Washington

UW Box Number 354322
Homepage Personal Home Page 
ORCID iD  0000-0002-5647-3160 

Research: selective inference and multiple testing, graphical models, distributional robustness, causality, latent variable modeling, optimization


Extremal graphical modeling with latent variables
Sebastian Engelke, Armeen Taeb
Extremal graphical models encode the conditional independence structure of multivariate extremes and provide a powerful tool for quantifying the risk of rare…

Model Selection over Partially Ordered Sets
Armeen Taeb, Peter BÜhlmann, Venkat Chandrasekaran
In problems such as variable selection and graph estimation, models are characterized by Boolean logical structure such as presence or absence of a variable or…

Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models
Tong Xu, Armeen Taeb, Simge KÜçÜkyavuz, Ali Shojaie
We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model…

Inverse Problems and Data Assimilation
Daniel Sanz-Alonso, Andrew M. Stuart, Armeen Taeb
We provide a clear and concise introduction to the subjects of inverse problems and data assimilation, and their inter-relations. The first part of our notes…

Provable concept learning for interpretable predictions using variational autoencoders
Armeen Taeb, Nicolo Ruggeri, Carina Schnuck, Fanny Yang
In safety-critical applications, practitioners are reluctant to trust neural networks when no interpretable explanations are available. Many attempts to…

Learning and scoring Gaussian latent variable causal models with unknown additive interventions
Armeen Taeb, Juan L. Gamella, Christina Heinze-Deml, Peter BÜhlmann
With observational data alone, causal structure learning is a challenging problem. The task becomes easier when having access to data collected from…

Learning Exponential Family Graphical Models with Latent Variables using Regularized Conditional Likelihood
Armeen Taeb, Parikshit Shah, Venkat Chandrasekaran
Fitting a graphical model to a collection of random variables given sample observations is a challenging task if the observed variables are influenced by…

False Discovery and Its Control in Low Rank Estimation
Armeen Taeb, Parikshit Shah, Venkat Chandrasekaran
Models specified by low-rank matrices are ubiquitous in contemporary applications. In many of these problem domains, the row/column space structure of a low…

Interpreting Latent Variables in Factor Models via Convex Optimization
Armeen Taeb, Venkat Chandrasekaran
Latent or unobserved phenomena pose a significant difficulty in data analysis as they induce complicated and confounding dependencies among a collection of…

California Reservoir Drought Sensitivity and Exhaustion Risk Using Statistical Graphical Models
Armeen Taeb, John T. Reager, Michael Turmon, Venkat Chandrasekaran
The ongoing California drought has highlighted the potential vulnerability of state water management infrastructure to multi-year dry intervals. Due to the…

Sufficient Dimension Reduction and Modeling Responses Conditioned on Covariates: An Integrated Approach via Convex Optimization
Armeen Taeb, Venkat Chandrasekaran
Given observations of a collection of covariates and responses $(Y, X) \in \mathbb{R}^p \times \mathbb{R}^q$, sufficient dimension reduction (SDR) techniques…

Maximin Analysis of Message Passing Algorithms for Recovering Block Sparse Signals
Armeen Taeb, Arian Maleki, Christoph Studer, Richard Baraniuk
We consider the problem of recovering a block (or group) sparse signal from an underdetermined set of random linear measurements, which appear in compressed…