Axiomatic Effect Propagation in Structural Causal Models
Journal of Machine Learning Research, 2024
flow-based decomposition of effect under a causal DAG
Publications by Tags
attribution
bayesian-methods
Bayesian Spiked Laplacian Graphs
Journal of Machine Learning Research, 2023
community detection and network modeling via a probablistic network model
A Bayesian Framework for Sparse Estimation in High-dimensional Mixed Frequency Vector Autoregressive Models
Statistica Sinica, 2023
vector autoregressive model for high-dimensional mixed frequency data under the sparsity assumption
The Bayesian Nested Lasso for Mixed Frequency Regression Models
Annals of Applied Statistics, 2023
joint estimation of mixed frequency data via a Bayesian approach
Bayesian Methodology for Adaptive Sparsity and Shrinkage in Regression
TBD, 2024
a data-adaptive method that performs model selection, covering the entire spectral from a sparse model to a dense one
A Generalized Bayesian Approach for High-dimensional Robust Regression with Serially Correlated Errors and Predictors
TBD, 2025
generalized Bayesian method for high-dimensional robust regression
bilevel-programming
A Penalty-based Method for Communication-Efficient Decentralized Bilevel Programming
Automatica, 2025
a distributed algorithm for bilevel-optimization
change-points
Multiple Change Point Detection in Reduced Rank High Dimensional Vector Autoregressive Models
Journal of the American Statistical Association, 2023
detecting and locating change points in high-dimensional Vector Autoregressive (VAR) models
Inference on the Change Point under a High Dimensional Covariance Shift
Journal of Machine Learning Research, 2023
algorithm for change point detection in the presence of covariate shift
deep-learning
A Multi-Task Encoder-Dual-Decoder Framework for Mixed Frequency Data Prediction
International Journal of Forecasting, 2023
DNN method for mixed-frequency data prediction
A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivity
Transactions on Machine Learning Research, 2024
DNN method for estimating the Granger causality of multiple dynamical systems via a VAE-based formulation
Deep Learning-based Approaches for State Space Models: A Selective Review
TBD, 2024
a review of deep-learning based SSMs for sequence modeling
dynamical-systems
Joint Learning of Linear Time-Invariant Dynamical Systems
Automatica, 2024
system identification of multiple related time-invariant linear dynamical systems
graphical-models
Estimation of Gaussian Directed Acyclic Graphs using Partial Ordering Information with Applications to DREAM3 Networks and Dairy Cattle Data
Annals of Applied Statistics, 2023
an efficient algorithm for DAG estimation in the presence of partial ordering
high-dimensional-statistics
Estimation of Gaussian Directed Acyclic Graphs using Partial Ordering Information with Applications to DREAM3 Networks and Dairy Cattle Data
Annals of Applied Statistics, 2023
an efficient algorithm for DAG estimation in the presence of partial ordering
Low Tree-rank Bayesian Vector Autoregression Models
Journal of Machine Learning Research, 2023
vectorautoregressive model assuming that Granger causal graph has a tree structure
A General Modeling Framework for Network Autoregressive Processes
Technometrics, 2023
a general framework for modeling network autoregressive processes
Structural Discovery with Partial Ordering Information for Time-Dependent Data with Convergence Guarantees
Journal of Computational and Graphical Statistics, 2024
an ADMM algorithm for high dimensional structural VAR estimation in the presence of partial ordering
A Functional Coefficients Network Autoregressive Model
Statistica Sinica, 2024
modeling for multivariate time series where node values depend on its own past and its neighbors
A High-dimensional Approach to Measure Connectivity in the Financial Sector
Annals of Applied Statistics, 2024
explore connectivity pattern amongst US financial institutions based on debiased Lasso
Bayesian Methodology for Adaptive Sparsity and Shrinkage in Regression
TBD, 2024
a data-adaptive method that performs model selection, covering the entire spectral from a sparse model to a dense one
A Generalized Bayesian Approach for High-dimensional Robust Regression with Serially Correlated Errors and Predictors
TBD, 2025
generalized Bayesian method for high-dimensional robust regression
mixed-frequency-data
A Bayesian Framework for Sparse Estimation in High-dimensional Mixed Frequency Vector Autoregressive Models
Statistica Sinica, 2023
vector autoregressive model for high-dimensional mixed frequency data under the sparsity assumption
The Bayesian Nested Lasso for Mixed Frequency Regression Models
Annals of Applied Statistics, 2023
joint estimation of mixed frequency data via a Bayesian approach
network-modeling
Bayesian Spiked Laplacian Graphs
Journal of Machine Learning Research, 2023
community detection and network modeling via a probablistic network model
optimization
DAdam: A Consensus-Based Distributed Adaptive Gradient Method for Online Optimization
IEEE Transactions on Signal Processing, 2022
a consensus-based distributed adaptive moment estimation method (DAdam) for online optimization over a decentralized network
Structural Discovery with Partial Ordering Information for Time-Dependent Data with Convergence Guarantees
Journal of Computational and Graphical Statistics, 2024
an ADMM algorithm for high dimensional structural VAR estimation in the presence of partial ordering
A Penalty-based Method for Communication-Efficient Decentralized Bilevel Programming
Automatica, 2025
a distributed algorithm for bilevel-optimization
review-paper
Deep Learning-based Approaches for State Space Models: A Selective Review
TBD, 2024
a review of deep-learning based SSMs for sequence modeling
time-series
A Bayesian Framework for Sparse Estimation in High-dimensional Mixed Frequency Vector Autoregressive Models
Statistica Sinica, 2023
vector autoregressive model for high-dimensional mixed frequency data under the sparsity assumption
The Bayesian Nested Lasso for Mixed Frequency Regression Models
Annals of Applied Statistics, 2023
joint estimation of mixed frequency data via a Bayesian approach
Low Tree-rank Bayesian Vector Autoregression Models
Journal of Machine Learning Research, 2023
vectorautoregressive model assuming that Granger causal graph has a tree structure
A Multi-Task Encoder-Dual-Decoder Framework for Mixed Frequency Data Prediction
International Journal of Forecasting, 2023
DNN method for mixed-frequency data prediction
A General Modeling Framework for Network Autoregressive Processes
Technometrics, 2023
a general framework for modeling network autoregressive processes
Structural Discovery with Partial Ordering Information for Time-Dependent Data with Convergence Guarantees
Journal of Computational and Graphical Statistics, 2024
an ADMM algorithm for high dimensional structural VAR estimation in the presence of partial ordering
A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivity
Transactions on Machine Learning Research, 2024
DNN method for estimating the Granger causality of multiple dynamical systems via a VAE-based formulation
A Functional Coefficients Network Autoregressive Model
Statistica Sinica, 2024
modeling for multivariate time series where node values depend on its own past and its neighbors
A High-dimensional Approach to Measure Connectivity in the Financial Sector
Annals of Applied Statistics, 2024
explore connectivity pattern amongst US financial institutions based on debiased Lasso
Deep Learning-based Approaches for State Space Models: A Selective Review
TBD, 2024
a review of deep-learning based SSMs for sequence modeling