Jiaqi Li

Time Series

(2024) L² Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM
J. Li, Likai Chen, Weining Wang, Wei Biao Wu
The Annals of Statistics (AOS). 52(2): 602-627.
[R code: R package 'L2hdchange']
(2024) Simultaneous Inference of a Partially Linear Model in Time Series
J. Li, Likai Chen, Kun Ho Kim, Tianwei Zhou
The Journal of Time Series Analysis (JTSA), to appear.
(2022) Change-Point Detection for High-Dimensional Time Series with Cross-Sectional Dependence
J. Li, Likai Chen, Jia He, Maggie Cheng, Wei Biao Wu
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), submitted.
Inference for Quantile Change Points in High-Dimensional Time Series (working paper)
J. Li, Likai Chen and Mengyu Xu
Adaptive Two-Way MOSUM: Testing for Multiscale Change Points in Spatial Time Series (working paper)
J. Li, Hangcen Zou, Michel F. C. Haddad, Likai Chen

Statistical Learning

(2025+) Online Inference for Quantiles by Constant Learning-Rate Stochastic Gradient Descent
Ziyang Wei, J. Li, Likai Chen, Wei Biao Wu
The 38th Annual Conference on Learning Theory (COLT 2025), under review.
(2024+) Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models
J. Li, Johannes Schmidt-Hieber, Wei Biao Wu
The Journal of Machine Learning Research (JMLR), under review.
[R code: Github 'Dropout_SGD']
(2024+) The Stochastic Gradient Descent from a Nonlinear Time Series Perspective
J. Li, Zhipeng Lou, Stefan Richter, Wei Biao Wu
The Annals of Statistics (AOS), revision submitted.
(2024+) L^p-Convergence of Stochastic Gradient Descent with a Mixture Learning Rate
Or Goldreich^*, Qianqian Lei^*, J. Li^*, Sung-Yi Lin^*, Wei Biao Wu
The Journal of Computational and Graphical Statistics, under review.
(2024) Probabilistic Guarantees of Stochastic Recursive Gradient in Non-Convex Finite Sum Problems
Yanjie Zhong^*, J. Li^*, Soumendra Lahiri
The 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD).
[Python code: Github 'Prob-SARAH-PyTorch']
Asymptotic Theory of Reinforcement Learning with a Constant Learning Rate (working paper)
J. Li, Zhipeng Lou, Susan Murphy, Wei Biao Wu
Online Inference for Quantile Change Points by Stochastic Gradient Descent (working paper)
J. Li, Lan Wang, Wei Biao Wu
Statistical Analysis of Adaptive Variance-Reduced Expectation-Maximization Algorithms Under the Polyak-Łojasiewicz Condition (working paper)
Yanjie Zhong, J. Li, Todd Kuffner
(2018) Design of Wi-Fi Indoor Positioning Model and Algorithm Based on Radial Basis Function with Optimal Shape Parameter
J. Li
IEEE 10th International Conference on Communication Software and Networks (ICCSN)
Oral presentation honored as "Best Talk", acceptance < 0.1%

Neuroimaging

(2024) Network Level Enrichment Provides a Framework for Biological Interpretation of Machine Learning Results
J. Li, Ari Segel, Xinyang Feng, Jiaxin Cindy Tu, Andy Eck, Kelsey King, Babatunde Adeyemo, Nicole R. Karcher, Likai Chen, Adam T. Eggebrecht, Muriah D. Wheelock
Network Neuroscience. 8(3): 762–790.
[MATLAB code: Github 'MachineLearning_NetworkLevelAnalysis']
(2024+) Early Life Neuroimaging: The Generalizability of Cortical Area Parcellations Across Development
JC Tu, M Myers, W Li, J. Li, X Wang, D Dierker, T Day, AZ Snyder, A Latham, JK Kenley, CM. Sobolewski, Y Wang, AK Labonte, E Feczko, O Kardan, LA Moore, CM Sylvester, DA Fair, JT Elison, BB Warner, DM Barch, CE Rogers, JL Luby, CD Smyser, EM Gordon, TO Laumann, AT Eggebrecht, MD Wheelock
Cerebral Cortex, revision submitted.
bioRxiv prerint:2024.09.09.612056.
[MATLAB code: Github 'AreaParcellationInfants']
(2024+) Inferring Vigilance Fluctuations from functional MRI Using Automated Sleep Stage Decoding (working paper)
Rezwana R. Razzaque, Jim Pollaro, Hong Chen, Donna Dierker, Zachary E. Markow, J. Li, Helmut Laufs, Muriah D. Wheelock
(2023+) Effects of functional network model definition on biomarker outcome prediction
Xinyang Feng^*, Wei Li^*, J. Li^*, Jiaxin C. Tu, Ari Segel, Xinyang Feng, Muriah D. Wheelock

Research

Research

Research

Time Series

Statistical Learning

Neuroimaging