1 citations · 1 across the 7 of their papers we have counts for
9 papers
Assumption-Lean Inference for Spectral Differential Network Analysis of High-Dimensional Time Series
Michael Hellstern, Byol Kim, Ali Shojaie
Network analysis for multivariate time series is popular in many fields, from neuroscience to seismology. The inverse spectral density is a common choice for time series network an…
Convex Mixed-Integer Programming for Causal Additive Models with Optimization and Statistical Guarantees
Xiaozhu Zhang, Nir Keret, Ali Shojaie +1
We study the problem of learning a directed acyclic graph from data generated according to an additive, non-linear structural equation model with Gaussian noise. We express each no…
GLM Inference with AI-Generated Synthetic Data Using Misspecified Linear Regression
Nir Keret, Ali Shojaie
Data privacy concerns have led to the growing interest in synthetic data, which strives to preserve the statistical properties of the original dataset while ensuring privacy by exc…
GREGoR: Accelerating Genomics for Rare Diseases
Moez Dawood, Ben Heavner, Marsha M. Wheeler +31
Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to…
Spectral Differential Network Analysis for High-Dimensional Time Series
Michael Hellstern, Byol Kim, Zaid Harchaoui +1
Spectral networks derived from multivariate time series data arise in many domains, from brain science to Earth science. Often, it is of interest to study how these networks change…
An Asymptotically Optimal Coordinate Descent Algorithm for Learning Bayesian Networks from Gaussian Models
Tong Xu, Simge Küçükyavuz, Ali Shojaie +1
This paper studies the problem of learning Bayesian networks from continuous observational data, generated according to a linear Gaussian structural equation model. We consider an…
Regularised Spectral Estimation for High-Dimensional Point Processes
Carla Pinkney, Carolina Euan, Alex Gibberd +1
Advances in modern technology have enabled the simultaneous recording of neural spiking activity, which statistically can be represented by a multivariate point process. We charact…
Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models
Tong Xu, Armeen Taeb, Simge Küçükyavuz +1
We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model. State-of-the-art st…
Learning Directed Acyclic Graphs from Partial Orderings
Ali Shojaie, Wenyu Chen
Directed acyclic graphs (DAGs) are commonly used to model causal relationships among random variables. In general, learning the DAG structure is both computationally and statistica…