Publications and Preprints

Research interest: I am broadly interested in statistical machine learning, where I develop reliable methods and theory for large-scale and complex statistical problems. In particular, I have been working on the following topics: uncertainty quantification, interpretable machine learning, statistical learning, ensemble methods, data integration, spectral methods, graph learning.

(*: equal contribution; †: student advisee.)

2026

LOCO-AdaMP: Built-in LOCO Inference for Adaptive Minipatch Ensembles with Enhanced Prediction
Yinan Cheng†, Lili Zheng
Under review

Model-Agnostic Feature Selection via LOCO-Guided Adaptive Minipatch Sampling
Xuhui Liu†, Lili Zheng
Under review

Cluster Quilting: Spectral Clustering for Patchwork Learning
Lili Zheng*, Andersen Chang*, Genevera I. Allen
Journal of the American Statistical Association, Theory and Methods, 2026

LOCO Feature Importance Inference without Data Splitting via Minipatch Ensembles
Luqin Gan*, Lili Zheng*, Genevera I. Allen
Under revision

2025

iLOCO: Distribution-Free Inference for Feature Interactions
Camille O. Little, Lili Zheng, Genevera I. Allen
Accepted to Transactions on Machine Learning Research (TMLR)

2024

Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity
Andersen Chang, Lili Zheng, Genevera I. Allen
Under review

Interpretable Machine Learning for Discovery: Statistical Challenges & Opportunities
Genevera I. Allen, Luqin Gan, Lili Zheng
Annual Review of Statistics and Its Application, 2024

2023

Joint Semi-Symmetric Tensor PCA for Integrating Multi-modal Populations of Networks
Jiaming Liu*, Lili Zheng*, Zhengwu Zhang, Genevera I. Allen
Under revision

High-dimensional Multi-class Classification with Presence-only Data
Lili Zheng, Garvesh Raskutti

Graphical Model Inference with Erosely Measured Data
Lili Zheng, Genevera I. Allen
Journal of the American Statistical Association, Theory and Methods, 2023 [Code]

Nonparanormal Graph Quilting with Applications to Calcium Imaging
Andersen Chang*, Lili Zheng*, Gautam Dasarathy, Genevera I. Allen
STAT, 2023

2022

Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
Hao Chen*, Lili Zheng*, Raed Al Kontar, Garvesh Raskutti
Journal of Machine Learning Research, 2022

Optimal High-order Tensor SVD via Tensor-Train Orthogonal Iteration
Yuchen Zhou, Anru R. Zhang, Lili Zheng, Yazhen Wang
IEEE Transactions on Information Theory, 2022 [Code]

A Low-Rank Tensor Completion Approach for Imputing Functional Neuronal Data from Multiple Recordings
Lili Zheng, Zachary T. Rewolinski, Genevera I. Allen
IEEE Data Science and Learning Workshop (DSLW), 2022

Learning Gaussian Graphical Models with Differing Pairwise Sample Sizes
Lili Zheng, Genevera I. Allen
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022

2021

Context-dependent Networks in Multivariate Time Series: Models, Methods, and Risk Bounds in High Dimensions
Lili Zheng, Garvesh Raskutti, Rebecca Willett, Benjamin Mark
Journal of Machine Learning Research, 2021 [Slides] [Code]

2020

Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes
Hao Chen*, Lili Zheng*, Raed Al Kontar, Garvesh Raskutti
Neural Information Processing Systems (NeurIPS), 2020

2019

Testing for High-dimensional Network Parameters in Auto-regressive Models
Lili Zheng, Garvesh Raskutti
Electronic Journal of Statistics, 2019 [Code]