284 citations · 587 across the 49 of their papers we have counts for
13 papers · 1 filter
Causal Discovery in High-Dimensional Point Process Networks with Hidden Nodes
Xu Wang, Ali Shojaie
Thanks to technological advances leading to near-continuous time observations, emerging multivariate point process data offer new opportunities for causal discovery. However, a key…
Statistical Inference for Networks of High-Dimensional Point Processes
Xu Wang, Mladen Kolar, Ali Shojaie
Fueled in part by recent applications in neuroscience, the multivariate Hawkes process has become a popular tool for modeling the network of interactions among high-dimensional poi…
Wavelet regression and additive models for irregularly spaced data
Asad Haris, Noah Simon, Ali Shojaie
We present a novel approach for nonparametric regression using wavelet basis functions. Our proposal, , can be applied to non-equispaced data with sample size no…
Generalized Score Matching for Non-Negative Data
Shiqing Yu, Mathias Drton, Ali Shojaie
A common challenge in estimating parameters of probability density functions is the intractability of the normalizing constant. While in such cases maximum likelihood estimation ma…
The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks
Arjun Sondhi, Ali Shojaie
We consider the task of estimating a high-dimensional directed acyclic graph, given observations from a linear structural equation model with arbitrary noise distribution. By explo…
Neural Granger Causality
Alex Tank, Ian Covert, Nicholas Foti +2
While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently…
An Efficient ADMM Algorithm for Structural Break Detection in Multivariate Time Series
Alex Tank, Emily B. Fox, Ali Shojaie
We present an efficient alternating direction method of multipliers (ADMM) algorithm for segmenting a multivariate non-stationary time series with structural breaks into stationary…
An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery
Alex Tank, Ian Cover, Nicholas J. Foti +2
While most classical approaches to Granger causality detection repose upon linear time series assumptions, many interactions in neuroscience and economics applications are nonlinea…
Joint Estimation of Precision Matrices in Heterogeneous Populations
Takumi Saegusa, Ali Shojaie
We introduce a general framework for estimation of inverse covariance, or precision, matrices from heterogeneous populations. The proposed framework uses a Laplacian shrinkage pena…
Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles
Ali Shojaie, Alexandra Jauhiainen, Michael Kallitsis +1
Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference c…
The Cluster Graphical Lasso for improved estimation of Gaussian graphical models
Kean Ming Tan, Daniela Witten, Ali Shojaie
We consider the task of estimating a Gaussian graphical model in the high-dimensional setting. The graphical lasso, which involves maximizing the Gaussian log likelihood subject to…
Discovering Graphical Granger Causality Using the Truncating Lasso Penalty
Ali Shojaie, George Michailidis
Components of biological systems interact with each other in order to carry out vital cell functions. Such information can be used to improve estimation and inference, and to obtai…
Penalized Likelihood Methods for Estimation of Sparse High Dimensional Directed Acyclic Graphs
Ali Shojaie, George Michailidis
Directed acyclic graphs (DAGs) are commonly used to represent causal relationships among random variables in graphical models. Applications of these models arise in the study of ph…