348 citations · 1.2k across the 81 of their papers we have counts for
11 papers · 1 filter
Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA
Michael Weylandt, George Michailidis
Network data are commonly collected in a variety of applications, representing either directly measured or statistically inferred connections between features of interest. In an in…
Joint Learning of Linear Time-Invariant Dynamical Systems
Aditya Modi, Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari +1
Linear time-invariant systems are very popular models in system theory and applications. A fundamental problem in system identification that remains rather unaddressed in extant li…
Sparse Partial Least Squares for Coarse Noisy Graph Alignment
Michael Weylandt, George Michailidis, T. Mitchell Roddenberry
Graph signal processing (GSP) provides a powerful framework for analyzing signals arising in a variety of domains. In many applications of GSP, multiple network structures are avai…
Automatic Registration and Clustering of Time Series
Michael Weylandt, George Michailidis
Clustering of time series data exhibits a number of challenges not present in other settings, notably the problem of registration (alignment) of observed signals. Typical approache…
Regularized and Smooth Double Core Tensor Factorization for Heterogeneous Data
Davoud Ataee Tarzanagh, George Michailidis
We introduce a general tensor model suitable for data analytic tasks for {\em heterogeneous} datasets, wherein there are joint low-rank structures within groups of observations, bu…
Online Distributed Estimation of Principal Eigenspaces
Davoud Ataee Tarzanagh, Mohamad Kazem Shirani Faradonbeh, George Michailidis
Principal components analysis (PCA) is a widely used dimension reduction technique with an extensive range of applications. In this paper, an online distributed algorithm is propos…
Sequential change-point detection in high-dimensional Gaussian graphical models
Hossein Keshavarz, George Michailidis, Yves Atchade
High dimensional piecewise stationary graphical models represent a versatile class for modelling time varying networks arising in diverse application areas, including biology, econ…
Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models
Subhabrata Majumdar, George Michailidis
The rapid development of high-throughput technologies has enabled the generation of data from biological or disease processes that span multiple layers, like genomic, proteomic or…
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…
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…