2 citations · 2 across the 6 of their papers we have counts for
6 papers
Generalized Criterion for Identifiability of Additive Noise Models Using Majorization
Aramayis Dallakyan, Yang Ni
The discovery of causal relationships from observational data is very challenging. Many recent approaches rely on complexity or uncertainty concepts to impose constraints on probab…
Unifying Sparse Estimation of Precision and Covariance Matrices
Aramayis Dallakyan, Mohsen Pourahmadi
Penalized maximum likelihood is a widely used approach to estimate a sparse precision or a sparse covariance matrix. A zero in the precision matrix encodes a conditional independen…
On Learning Time Series Summary DAGs: A Frequency Domain Approach
Aramayis Dallakyan
The fields of time series and graphical models emerged and advanced separately. Previous work on the structure learning of continuous and real-valued time series utilizes the time…
Time Series Graphical Lasso and Sparse VAR Estimation
Aramayis Dallakyan, Rakheon Kim, Mohsen Pourahmadi
We improve upon the two-stage sparse vector autoregression (sVAR) method in Davis et al. (2016) by proposing an alternative two-stage modified sVAR method which relies on time seri…
Learning Bayesian Networks through Birkhoff Polytope: A Relaxation Method
Aramayis Dallakyan, Mohsen Pourahmadi
We establish a novel framework for learning a directed acyclic graph (DAG) when data are generated from a Gaussian, linear structural equation model. It consists of two parts: (1)…
Fused-Lasso Regularized Cholesky Factors of Large Nonstationary Covariance Matrices of Longitudinal Data
Aramayis Dallakyan, Mohsen Pourahmadi
Smoothness of the subdiagonals of the Cholesky factor of large covariance matrices is closely related to the degrees of nonstationarity of autoregressive models for time series and…