Peng Zheng

According to our database, Peng Zheng authored at least 15 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Relaxation Approach to Feature Selection for Linear Mixed Effects Models.

Aleksei Sholokhov, James V. Burke, Damian F. Santomauro, Peng Zheng, Aleksandr Y. Aravkin

J. Comput. Graph. Stat., 2024

Uncertainty Quantification under Noisy Constraints, with Applications to Raking.

Ariane Ducellier, Alexander Hsu, Parkes Kendrick, Bill Gustafson, Laura Dwyer-Lindgren, Christopher Murray, Peng Zheng, Aleksandr Y. Aravkin

CoRR, 2024

2023

pysr3: A Python Package for Sparse Relaxed Regularized Regression.

Aleksei Sholokhov, Peng Zheng, Aleksandr Y. Aravkin

J. Open Source Softw., June, 2023

2022

Robust and Scalable Methods for the Dynamic Mode Decomposition.

Travis Askham, Peng Zheng, Aleksandr Y. Aravkin, J. Nathan Kutz

SIAM J. Appl. Dyn. Syst., 2022

2021

Efficient Robust Parameter Identification in Generalized Kalman Smoothing Models.

Jonathan Jonker, Peng Zheng, Aleksandr Y. Aravkin

IEEE Trans. Autom. Control., 2021

Estimating Shape Parameters of Piecewise Linear-Quadratic Problems.

Peng Zheng, Karthikeyan Natesan Ramamurthy, Aleksandr Y. Aravkin

Open J. Math. Optim., 2021

Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks.

Germán Abrevaya, Guillaume Dumas, Aleksandr Y. Aravkin, Peng Zheng, Jean-Christophe Gagnon-Audet, James R. Kozloski, Pablo Polosecki, Guillaume Lajoie, David D. Cox, Silvina Ponce Dawson, Guillermo A. Cecchi, Irina Rish

Neural Comput., 2021

Trimmed Constrained Mixed Effects Models: Formulations and Algorithms.

Peng Zheng, Ryan Barber, Reed J. D. Sorensen, Christopher J. L. Murray, Aleksandr Y. Aravkin

J. Comput. Graph. Stat., 2021

2020

Sparse Principal Component Analysis via Variable Projection.

N. Benjamin Erichson, Peng Zheng, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin

SIAM J. Appl. Math., 2020

A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data.

Kathleen P. Champion, Peng Zheng, Aleksandr Y. Aravkin, Steven L. Brunton, J. Nathan Kutz

IEEE Access, 2020

2019

A Unified Framework for Sparse Relaxed Regularized Regression: SR3.

Peng Zheng, Travis Askham, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin

IEEE Access, 2019

Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning.

Jihun Yun, Peng Zheng, Eunho Yang, Aurélie C. Lozano, Aleksandr Y. Aravkin

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Sparse Relaxed Regularized Regression: SR3.

Peng Zheng, Travis Askham, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin

CoRR, 2018

Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM.

Germán Abrevaya, Aleksandr Y. Aravkin, Guillermo A. Cecchi, Irina Rish, Pablo Polosecki, Peng Zheng, Silvina Ponce Dawson

CoRR, 2018

2017

Learning Robust Representations for Computer Vision.

Peng Zheng, Aleksandr Y. Aravkin, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy

Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops, 2017