High dimensional Vector Autoregressions (VAR) have received a lot of int...
Bilevel programming has recently received attention in the literature, d...
Recently, high dimensional vector auto-regressive models (VAR), have
att...
Existing methods of explainable AI and interpretable ML cannot explain c...
Vector autoregressions have been widely used for modeling and analysis o...
In a number of application domains, one observes a sequence of network d...
The paper addresses joint sparsity selection in the regression coefficie...
The fast transmission rate of COVID-19 worldwide has made this virus the...
Learning the parameters of a linear time-invariant dynamical system (LTI...
The paper develops a general flexible framework for Network Autoregressi...
Logistic regression is one of the most fundamental methods for modeling ...
We study the problem of detecting and locating change points in
high-dim...
There is increasing interest in identifying changes in the underlying st...
We consider the problem of constructing confidence intervals for the
loc...
Min-max saddle point games have recently been intensely studied, due to ...
Vector Auto-Regressive (VAR) models capture lead-lag temporal dynamics o...
Adaptive momentum methods have recently attracted a lot of attention for...
Graph signal processing (GSP) provides a powerful framework for analyzin...
Clustering of time series data exhibits a number of challenges not prese...
Distributed systems serve as a key technological infrastructure for
moni...
In this paper, we design and analyze a new family of adaptive subgradien...
Sparse estimation of the precision matrix under high-dimensional scaling...
The problem of identifying change points in high-dimensional Gaussian
gr...
A factor-augmented vector autoregressive (FAVAR) model is defined by a V...
We consider the estimation of approximate factor models for time series ...
We introduce a general tensor model suitable for data analytic tasks for...
In the network data analysis, it is common to encounter a large populati...
As electronically stored data grow in daily life, obtaining novel and
re...
Principal components analysis (PCA) is a widely used dimension reduction...
Data-driven control strategies for dynamical systems with unknown parame...
We study the problem of detecting a common change point in large panel d...
In decision making problems for continuous state and action spaces, line...
Estimating a directed acyclic graph (DAG) from observational data repres...
The problem of joint estimation of multiple graphical models from high
d...
Adaptive gradient-based optimization methods such as ADAGRAD, RMSPROP, a...
Network modeling of high-dimensional time series data is a key learning ...
We consider the problem of estimating the location of a single change po...
Design of adaptive algorithms for simultaneous regulation and estimation...
Stabilization of linear systems with unknown dynamics is a canonical pro...
Adaptive regulation of linear systems represents a canonical problem in
...
High dimensional piecewise stationary graphical models represent a versa...
The rapid development of high-throughput technologies has enabled the
ge...
We consider the classical problem of control of linear systems with quad...
Reconstructing transcriptional regulatory networks is an important task ...
The robustness and integrity of IP networks require efficient tools for
...
Time series of graphs are increasingly prevalent in modern data and pose...
Directed acyclic graphs (DAGs) are commonly used to represent causal
rel...