research
∙
11/14/2022
Robust Estimation of Sparse, High Dimensional Time Series with Polynomial Tails
High dimensional Vector Autoregressions (VAR) have received a lot of int...
share
research
∙
11/08/2022
A Penalty Based Method for Communication-Efficient Decentralized Bilevel Programming
Bilevel programming has recently received attention in the literature, d...
research
∙
09/19/2022
Optimal Sparse Estimation of High Dimensional Heavy-tailed Time Series
Recently, high dimensional vector auto-regressive models (VAR), have att...
research
∙
06/26/2022
Explaining the root causes of unit-level changes
Existing methods of explainable AI and interpretable ML cannot explain c...
research
∙
04/04/2022
Low Tree-Rank Bayesian Vector Autoregression Model
Vector autoregressions have been widely used for modeling and analysis o...
research
∙
02/09/2022
A Coupled CP Decomposition for Principal Components Analysis of Symmetric Networks
In a number of application domains, one observes a sequence of network d...
research
∙
01/14/2022
A generalized likelihood based Bayesian approach for scalable joint regression and covariance selection in high dimensions
The paper addresses joint sparsity selection in the regression coefficie...
research
∙
12/21/2021
Hybrid Modeling of Regional COVID-19 Transmission Dynamics in the U.S
The fast transmission rate of COVID-19 worldwide has made this virus the...
research
∙
12/21/2021
Joint Learning of Linear Time-Invariant Dynamical Systems
Learning the parameters of a linear time-invariant dynamical system (LTI...
research
∙
10/18/2021
A General Modeling Framework for Network Autoregressive Processes
The paper develops a general flexible framework for Network Autoregressi...
research
∙
10/07/2021
High Dimensional Logistic Regression Under Network Dependence
Logistic regression is one of the most fundamental methods for modeling ...
research
∙
09/30/2021
Multiple Change Point Detection in Reduced Rank High Dimensional Vector Autoregressive Models
We study the problem of detecting and locating change points in high-dim...
research
∙
09/30/2021
A Fast Detection Method of Break Points in Effective Connectivity Networks
There is increasing interest in identifying changes in the underlying st...
research
∙
07/19/2021
Inference for Change Points in High Dimensional Mean Shift Models
We consider the problem of constructing confidence intervals for the loc...
research
∙
06/10/2021
A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max Optimization Problems
Min-max saddle point games have recently been intensely studied, due to ...
research
∙
05/23/2021
Multiple Change Point Detection in Structured VAR Models: the VARDetect R Package
Vector Auto-Regressive (VAR) models capture lead-lag temporal dynamics o...
research
∙
04/26/2021
Solving a class of non-convex min-max games using adaptive momentum methods
Adaptive momentum methods have recently attracted a lot of attention for...
research
∙
04/06/2021
Sparse Partial Least Squares for Coarse Noisy Graph Alignment
Graph signal processing (GSP) provides a powerful framework for analyzin...
research
∙
12/08/2020
Automatic Registration and Convex Clustering of Time Series
Clustering of time series data exhibits a number of challenges not prese...
research
∙
12/03/2020
A semi-parametric model for target localization in distributed systems
Distributed systems serve as a key technological infrastructure for moni...
research
∙
05/19/2020
Adaptive First-and Zeroth-order Methods for Weakly Convex Stochastic Optimization Problems
In this paper, we design and analyze a new family of adaptive subgradien...
research
∙
05/18/2020
B-CONCORD – A scalable Bayesian high-dimensional precision matrix estimation procedure
Sparse estimation of the precision matrix under high-dimensional scaling...
research
∙
03/16/2020
Online detection of local abrupt changes in high-dimensional Gaussian graphical models
The problem of identifying change points in high-dimensional Gaussian gr...
research
∙
12/09/2019
Regularized Estimation of High-dimensional Factor-Augmented Autoregressive (FAVAR) Models
A factor-augmented vector autoregressive (FAVAR) model is defined by a V...
research
∙
12/09/2019
Approximate Factor Models with Strongly Correlated Idiosyncratic Errors
We consider the estimation of approximate factor models for time series ...
research
∙
11/24/2019
Regularized and Smooth Double Core Tensor Factorization for Heterogeneous Data
We introduce a general tensor model suitable for data analytic tasks for...
research
∙
10/06/2019
Spiked Laplacian Graphs: Bayesian Community Detection in Heterogeneous Networks
In the network data analysis, it is common to encounter a large populati...
research
∙
06/17/2019
Analyses of Multi-collection Corpora via Compound Topic Modeling
As electronically stored data grow in daily life, obtaining novel and re...
research
∙
05/17/2019
Online Distributed Estimation of Principal Eigenspaces
Principal components analysis (PCA) is a widely used dimension reduction...
research
∙
05/16/2019
Randomized Algorithms for Data-Driven Stabilization of Stochastic Linear Systems
Data-driven control strategies for dynamical systems with unknown parame...
research
∙
04/25/2019
Change Point Estimation in Panel Data with Temporal and Cross-sectional Dependence
We study the problem of detecting a common change point in large panel d...
research
∙
03/14/2019
On Applications of Bootstrap in Continuous Space Reinforcement Learning
In decision making problems for continuous state and action spaces, line...
research
∙
02/14/2019
Estimation of Gaussian directed acyclic graphs using partial ordering information with an application to dairy cattle data
Estimating a directed acyclic graph (DAG) from observational data repres...
research
∙
02/10/2019
A Bayesian Approach to Joint Estimation of Multiple Graphical Models
The problem of joint estimation of multiple graphical models from high d...
research
∙
01/25/2019
DADAM: A Consensus-based Distributed Adaptive Gradient Method for Online Optimization
Adaptive gradient-based optimization methods such as ADAGRAD, RMSPROP, a...
research
∙
12/09/2018
Low Rank and Structured Modeling of High-dimensional Vector Autoregressions
Network modeling of high-dimensional time series data is a key learning ...
research
∙
12/07/2018
Change Point Estimation in a Dynamic Stochastic Block Model
We consider the problem of estimating the location of a single change po...
research
∙
11/10/2018
Input Perturbations for Adaptive Regulation and Learning
Design of adaptive algorithms for simultaneous regulation and estimation...
research
∙
07/22/2018
Finite Time Adaptive Stabilization of LQ Systems
Stabilization of linear systems with unknown dynamics is a canonical pro...
research
∙
06/28/2018
On Optimality of Adaptive Linear-Quadratic Regulators
Adaptive regulation of linear systems represents a canonical problem in ...
research
∙
06/20/2018
Sequential change-point detection in high-dimensional Gaussian graphical models
High dimensional piecewise stationary graphical models represent a versa...
research
∙
03/09/2018
Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models
The rapid development of high-throughput technologies has enabled the ge...
research
∙
11/20/2017
Finite Time Analysis of Optimal Adaptive Policies for Linear-Quadratic Systems
We consider the classical problem of control of linear systems with quad...
research
∙
12/02/2013
Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles
Reconstructing transcriptional regulatory networks is an important task ...
research
∙
06/24/2013
A State-Space Approach for Optimal Traffic Monitoring via Network Flow Sampling
The robustness and integrity of IP networks require efficient tools for ...
research
∙
05/30/2013
Structural and Functional Discovery in Dynamic Networks with Non-negative Matrix Factorization
Time series of graphs are increasingly prevalent in modern data and pose...
research
∙
11/28/2009
