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Found 31 papers, 6 papers with code
A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivity
Granger causality has been widely used in various application domains to capture lead-lag relationships amongst the components of complex dynamical systems, and the focus in extant literature has been on a single dynamical system.
A Penalty-Based Method for Communication-Efficient Decentralized Bilevel Programming
A key feature of the proposed algorithm is to estimate the hyper-gradient of the penalty function via decentralized computation of matrix-vector products and few vector communications, which is then integrated within an alternating algorithm to obtain finite-time convergence analysis under different convexity assumptions.
Explaining the root causes of unit-level changes
Existing methods of explainable AI and interpretable ML cannot explain change in the values of an output variable for a statistical unit in terms of the change in the input values and the change in the "mechanism" (the function transforming input to output).
Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA
Remarkably, we show that SS-TPCA achieves the same estimation accuracy as classical matrix PCA, with error proportional to the square root of the number of vertices in the network and not the number of edges as might be expected.
Joint Learning of Linear Time-Invariant Dynamical Systems
Linear time-invariant systems are very popular models in system theory and applications.
Inference for Change Points in High Dimensional Mean Shift Models
Component-wise distributions are characterized under both vanishing and non-vanishing jump size regimes, while joint distributions for any finite subset of change point estimates are characterized under the latter regime, which also yields asymptotic independence of these estimates.
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 their wide range of applications, including training Generative Adversarial Networks (GANs).
Solving a class of non-convex min-max games using adaptive momentum methods
Adaptive momentum methods have recently attracted a lot of attention for training of deep neural networks.
Sparse Partial Least Squares for Coarse Noisy Graph Alignment
Graph signal processing (GSP) provides a powerful framework for analyzing signals arising in a variety of domains.
Automatic Registration and Clustering of Time Series
Clustering of time series data exhibits a number of challenges not present in other settings, notably the problem of registration (alignment) of observed signals.
A semi-parametric model for target localization in distributed systems
In this paper, we adopt a nonparametric approach that only assumes that the signal is nonincreasing as function of the distance between the sensor and the target.
Disaster Response
Methodology
Adaptive First-and Zeroth-order Methods for Weakly Convex Stochastic Optimization Problems
In this paper, we design and analyze a new family of adaptive subgradient methods for solving an important class of weakly convex (possibly nonsmooth) stochastic optimization problems.
Inference on the Change Point for High Dimensional Dynamic Graphical Models
We develop an estimator for the change point parameter for a dynamically evolving graphical model, and also obtain its asymptotic distribution under high dimensional scaling.
Online detection of local abrupt changes in high-dimensional Gaussian graphical models
The problem of identifying change points in high-dimensional Gaussian graphical models (GGMs) in an online fashion is of interest, due to new applications in biology, economics and social sciences.
Regularized Estimation of High-dimensional Factor-Augmented Autoregressive (FAVAR) Models
A factor-augmented vector autoregressive (FAVAR) model is defined by a VAR equation that captures lead-lag correlations amongst a set of observed variables $X$ and latent factors $F$, and a calibration equation that relates another set of observed variables $Y$ with $F$ and $X$.
Methodology Econometrics
Regularized and Smooth Double Core Tensor Factorization for Heterogeneous Data
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, but also discriminative structures across different groups.
Analyses of Multi-collection Corpora via Compound Topic Modeling
The usability study on some real-world corpora illustrates the superiority of cLDA to explore the underlying topics automatically but also model their connections and variations across multiple collections.
Online Distributed Estimation of Principal Eigenspaces
Principal components analysis (PCA) is a widely used dimension reduction technique with an extensive range of applications.
Randomized Algorithms for Data-Driven Stabilization of Stochastic Linear Systems
We provide numerical analyses for the performance of two methods: stochastic feedback, and stochastic parameter.
On Applications of Bootstrap in Continuous Space Reinforcement Learning
In decision making problems for continuous state and action spaces, linear dynamical models are widely employed.
DADAM: A Consensus-based Distributed Adaptive Gradient Method for Online Optimization
Adaptive gradient-based optimization methods such as \textsc{Adagrad}, \textsc{Rmsprop}, and \textsc{Adam} are widely used in solving large-scale machine learning problems including deep learning.
Change Point Estimation in a Dynamic Stochastic Block Model
Once the change point is identified, in the second step, all network data before and after it are used together with a clustering algorithm to obtain the corresponding community structures and subsequently estimate the generating stochastic block model parameters.
Input Perturbations for Adaptive Control and Learning
This paper studies adaptive algorithms for simultaneous regulation (i. e., control) and estimation (i. e., learning) of Multiple Input Multiple Output (MIMO) linear dynamical systems.
Finite Time Adaptive Stabilization of LQ Systems
There are only a few existing non-asymptotic results and a full treatment of the problem is not currently available.
Sequential change-point detection in high-dimensional Gaussian graphical models
High dimensional piecewise stationary graphical models represent a versatile class for modelling time varying networks arising in diverse application areas, including biology, economics, and social sciences.
Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models
Following this, we develop a debiasing technique and asymptotic distributions of inter-layer directed edge weights that utilize already computed neighborhood selection coefficients for nodes in the upper layer.
Optimism-Based Adaptive Regulation of Linear-Quadratic Systems
The main challenge for adaptive regulation of linear-quadratic systems is the trade-off between identification and control.
Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles
The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state gene expression data.
A State-Space Approach for Optimal Traffic Monitoring via Network Flow Sampling
The robustness and integrity of IP networks require efficient tools for traffic monitoring and analysis, which scale well with traffic volume and network size.
Structural and Functional Discovery in Dynamic Networks with Non-negative Matrix Factorization
Time series of graphs are increasingly prevalent in modern data and pose unique challenges to visual exploration and pattern extraction.
Penalized Principal Component Regression on Graphs for Analysis of Subnetworks
Network models are widely used to capture interactions among component of complex systems, such as social and biological.
