
Adaptive Firstand Zerothorder Methods for Weakly Convex Stochastic Optimization Problems
In this paper, we design and analyze a new family of adaptive subgradien...
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BCONCORD – A scalable Bayesian highdimensional precision matrix estimation procedure
Sparse estimation of the precision matrix under highdimensional scaling...
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Online detection of local abrupt changes in highdimensional Gaussian graphical models
The problem of identifying change points in highdimensional Gaussian gr...
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Regularized Estimation of Highdimensional FactorAugmented Autoregressive (FAVAR) Models
A factoraugmented vector autoregressive (FAVAR) model is defined by a V...
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Approximate Factor Models with Strongly Correlated Idiosyncratic Errors
We consider the estimation of approximate factor models for time series ...
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Regularized and Smooth Double Core Tensor Factorization for Heterogeneous Data
We introduce a general tensor model suitable for data analytic tasks for...
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Spiked Laplacian Graphs: Bayesian Community Detection in Heterogeneous Networks
In the network data analysis, it is common to encounter a large populati...
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Analyses of Multicollection Corpora via Compound Topic Modeling
As electronically stored data grow in daily life, obtaining novel and re...
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Online Distributed Estimation of Principal Eigenspaces
Principal components analysis (PCA) is a widely used dimension reduction...
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Randomized Algorithms for DataDriven Stabilization of Stochastic Linear Systems
Datadriven control strategies for dynamical systems with unknown parame...
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Change Point Estimation in Panel Data with Temporal and Crosssectional Dependence
We study the problem of detecting a common change point in large panel d...
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On Applications of Bootstrap in Continuous Space Reinforcement Learning
In decision making problems for continuous state and action spaces, line...
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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...
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A Bayesian Approach to Joint Estimation of Multiple Graphical Models
The problem of joint estimation of multiple graphical models from high d...
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DADAM: A Consensusbased Distributed Adaptive Gradient Method for Online Optimization
Adaptive gradientbased optimization methods such as ADAGRAD, RMSPROP, a...
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Low Rank and Structured Modeling of Highdimensional Vector Autoregressions
Network modeling of highdimensional time series data is a key learning ...
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Change Point Estimation in a Dynamic Stochastic Block Model
We consider the problem of estimating the location of a single change po...
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Input Perturbations for Adaptive Regulation and Learning
Design of adaptive algorithms for simultaneous regulation and estimation...
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Finite Time Adaptive Stabilization of LQ Systems
Stabilization of linear systems with unknown dynamics is a canonical pro...
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On Optimality of Adaptive LinearQuadratic Regulators
Adaptive regulation of linear systems represents a canonical problem in ...
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Sequential changepoint detection in highdimensional Gaussian graphical models
High dimensional piecewise stationary graphical models represent a versa...
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Joint Estimation and Inference for Data Integration Problems based on Multiple Multilayered Gaussian Graphical Models
The rapid development of highthroughput technologies has enabled the ge...
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Finite Time Analysis of Optimal Adaptive Policies for LinearQuadratic Systems
We consider the classical problem of control of linear systems with quad...
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Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles
Reconstructing transcriptional regulatory networks is an important task ...
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A StateSpace Approach for Optimal Traffic Monitoring via Network Flow Sampling
The robustness and integrity of IP networks require efficient tools for ...
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Structural and Functional Discovery in Dynamic Networks with Nonnegative Matrix Factorization
Time series of graphs are increasingly prevalent in modern data and pose...
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Penalized Likelihood Methods for Estimation of Sparse High Dimensional Directed Acyclic Graphs
Directed acyclic graphs (DAGs) are commonly used to represent causal rel...
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George Michailidis
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