Posts by Collection

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

Link to Paper

Bayesian Spiked Laplacian Graphs

Journal of Machine Learning Research, 2023

community detection and network modeling via a probablistic network model

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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

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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

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Inference on the Change Point under a High Dimensional Covariance Shift

Journal of Machine Learning Research, 2023

an algorithm for change point detection in the presence of covariate shift

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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

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The Bayesian Nested Lasso for Mixed Frequency Regression Models

Annals of Applied Statistics, 2023

joint estimation of mixed frequency data via a Bayesian approach

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Low Tree-rank Bayesian Vector Autoregression Models

Journal of Machine Learning Research, 2023

estimating a vector autoregressive (VAR) model assuming that Granger causal graph has a tree structure

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A Multi-Task Encoder-Dual-Decoder Framework for Mixed Frequency Data Prediction

International Journal of Forecasting, 2023

DNN method for mixed-frequency data prediction

Link to Paper | Code Repo

A General Modeling Framework for Network Autoregressive Processes

Technometrics, 2023

a general framework for modeling network autoregressive processes

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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

Link to Paper | Code Repo

A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivity

Transactions on Machine Learning Research (TMLR), 2024

DNN method for estimating the Granger causality of multiple dynamical systems via a VAE-based formulation

Link to Paper | Code Repo

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

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Axiomatic Effect Propagation in Structural Causal Models

Journal of Machine Learning Research, 2024

flow-based decomposition of effect under a causal DAG

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High Dimensional Logistic Regression Under Network Dependence

Journal of Machine Learning Research, 2024

Ising model with peer-effect from the network and the effect from covariates

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A High-dimensional Approach to Measure Connectivity in the Financial Sector

Annals of Applied Statistics, 2024

explore connectivity patterns amongst US financial institutions based on debiased Lasso

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Joint Learning of Linear Time-Invariant Dynamical Systems

Automatica, 2024

system identification of multiple related time-invariant linear dynamical systems

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Bayesian Methodology for Adaptive Sparsity and Shrinkage in Regression

TBD, 2024

a data-adaptive method that performs model selection, covering the spectrum where the model collection spans from a sparse to a dense one

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Deep Learning-based Approaches for State Space Models: A Selective Review

TBD, 2024

a review of deep-learning based SSMs for sequence modeling

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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

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A Penalty-based Method for Communication-Efficient Decentralized Bilevel Programming

Automatica, 2025

a distributed algorithm for bilevel-optimization

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Neural Network-Based Change Point Detection for Large-Scale Time-Evolving Data

TBD, 2025

neural network-based method for detecting change points for multivariate time series

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Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees

Transactions on Machine Learning Research (TMLR), 2025

estimate covariate-dependent graphical models using DNN with PAC guarantees

Link to Paper | Code Repo

STATS 102B: Intro to Computing and Optimization

Undergraduate course, UCLA, 2025

This course is offered in Spring Quarter 2025, Spring Quarter 2024, Spring Quarter 2023

STATS 202B: Matrix Algebra and Optimization

Graduate course, UCLA, 2025

This course introduces students to algorithms and their theoretical underpinnings extensively used in modern machine learning. It covers

• first order methods, including gradient descent and its variants;
• conditions for optimality for convex;
• constrained optimization problems and basics of duality theory;
• challenges posed by non-convex problems.

STATS 417: Models in Finance

Graduate course, UCLA, 2025

This course is offered in Winter Quarter 2025, Winter Quarter 2024