publications | Jie Chen (陈捷)

2025

Lessons Learned: A Multi-Agent Framework for Code LLMs to Learn and Improve

Yuanzhe Liu, Ryan Deng, Tim Kaler, Xuhao Chen, Charles E. Leiserson, Yao Ma, and Jie Chen

In Advances in Neural Information Processing Systems 38 (NeurIPS), 2025

@inproceedings{lessonl,
  author = {Liu, Yuanzhe and Deng, Ryan and Kaler, Tim and Chen, Xuhao and Leiserson, Charles E. and Ma, Yao and Chen, Jie},
  title = {Lessons Learned: A Multi-Agent Framework for Code {LLM}s to Learn and Improve},
  booktitle = {Advances in Neural Information Processing Systems 38 (NeurIPS)},
  year = {2025},
}

Optimization

Compressed Decentralized Momentum Stochastic Gradient Methods for Nonconvex Optimization

Wei Liu, Anweshit Panda, Ujwal Pandey, Christopher Brissette, Yikang Shen, George Slota, Naigang Wang, Jie Chen, and Yangyang Xu

In Transactions on Machine Learning Research (TMLR), 2025

PDF Bib PROCEEDINGS

@inproceedings{DAMSCo,
  author = {Liu, Wei and Panda, Anweshit and Pandey, Ujwal and Brissette, Christopher and Shen, Yikang and Slota, George and Wang, Naigang and Chen, Jie and Xu, Yangyang},
  title = {Compressed Decentralized Momentum Stochastic Gradient Methods for Nonconvex Optimization},
  booktitle = {Transactions on Machine Learning Research (TMLR)},
  year = {2025},
}

ML/AI

Directed Graph Grammars for Sequence-based Learning

Michael Sun, Orion Foo, Gang Liu, Wojciech Matusik, and Jie Chen

In Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025

PDF Bib PROCEEDINGS

@inproceedings{DIGGED,
  author = {Sun, Michael and Foo, Orion and Liu, Gang and Matusik, Wojciech and Chen, Jie},
  title = {Directed Graph Grammars for Sequence-based Learning},
  booktitle = {Proceedings of the 42nd International Conference on Machine Learning (ICML)},
  year = {2025},
}

ML/AI

Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages

Michael Sun, Weize Yuan, Gang Liu, Wojciech Matusik, and Jie Chen

In Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025

PDF Bib PROCEEDINGS

@inproceedings{FMG,
  author = {Sun, Michael and Yuan, Weize and Liu, Gang and Matusik, Wojciech and Chen, Jie},
  title = {Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages},
  booktitle = {Proceedings of the 42nd International Conference on Machine Learning (ICML)},
  year = {2025},
}

Application

LLM-Empowered Literature Mining for Material Substitution Studies in Sustainable Concrete

Yifei Duan, Yixi Tian, Soumya Ghosh, Vineeth Venugopal, Jie Chen, and Elsa A. Olivetti

Resources, Conservation & Recycling (RCR), 221:108379, 2025

PDF Bib DOI

@article{llm.lit.mining,
  author = {Duan, Yifei and Tian, Yixi and Ghosh, Soumya and Venugopal, Vineeth and Chen, Jie and Olivetti, Elsa A.},
  title = {{LLM}-Empowered Literature Mining for Material Substitution Studies in Sustainable Concrete},
  journal = {Resources, Conservation \& Recycling (RCR)},
  volume = {221},
  pages = {108379},
  year = {2025},
  doi = {10.1016/j.resconrec.2025.108379}
}

Scientific Computing

Graph Neural Preconditioners for Iterative Solutions of Sparse Linear Systems

Jie Chen

In Proceedings of the Thirteenth International Conference on Learning Representations (ICLR), 2025

PDF Bib PROCEEDINGS Code Slides Poster

@inproceedings{GNP,
  author = {Chen, Jie},
  title = {Graph Neural Preconditioners for Iterative Solutions of Sparse Linear Systems},
  booktitle = {Proceedings of the Thirteenth International Conference on Learning Representations (ICLR)},
  year = {2025},
}

ML/AI

Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning

Gang Liu, Michael Sun, Wojciech Matusik, Meng Jiang, and Jie Chen

In Proceedings of the Thirteenth International Conference on Learning Representations (ICLR), 2025

PDF Bib PROCEEDINGS Media

@inproceedings{llamole,
  author = {Liu, Gang and Sun, Michael and Matusik, Wojciech and Jiang, Meng and Chen, Jie},
  title = {Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning},
  booktitle = {Proceedings of the Thirteenth International Conference on Learning Representations (ICLR)},
  year = {2025},
}

ML/AI

Procedural Synthesis of Synthesizable Molecules

Michael Sun, Alston Lo, Minghao Guo, Jie Chen, Connor W. Coley, and Wojciech Matusik

In Proceedings of the Thirteenth International Conference on Learning Representations (ICLR), 2025

PDF Bib PROCEEDINGS

@inproceedings{procedural,
  author = {Sun, Michael and Lo, Alston and Guo, Minghao and Chen, Jie and Coley, Connor W. and Matusik, Wojciech},
  title = {Procedural Synthesis of Synthesizable Molecules},
  booktitle = {Proceedings of the Thirteenth International Conference on Learning Representations (ICLR)},
  year = {2025},
}

ML/AI

Neighborhood Sampling Does Not Learn the Same Graph Neural Network

Zehao Niu, Mihai Anitescu, and Jie Chen

Preprint arXiv:2509.22868, 2025

arXiv Bib

@misc{gntk,
  author = {Niu, Zehao and Anitescu, Mihai and Chen, Jie},
  title = {Neighborhood Sampling Does Not Learn the Same Graph Neural Network},
  howpublished = {Preprint arXiv:2509.22868},
  year = {2025},
}

ML/AI

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks

Ziyuan Tang and Jie Chen

Preprint arXiv:2506.14098, 2025

arXiv Bib

@misc{rwpt,
  author = {Tang, Ziyuan and Chen, Jie},
  title = {Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks},
  howpublished = {Preprint arXiv:2506.14098},
  year = {2025},
}

2024

ML/AI

Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series

Giangiacomo Mercatali, Andre Freitas, and Jie Chen

In Advances in Neural Information Processing Systems 37 (NeurIPS), 2024

PDF Bib PROCEEDINGS

@inproceedings{gneuralflow,
  author = {Mercatali, Giangiacomo and Freitas, Andre and Chen, Jie},
  title = {Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series},
  booktitle = {Advances in Neural Information Processing Systems 37 (NeurIPS)},
  year = {2024},
}

Application

Identifying Money Laundering Subgraphs on the Blockchain

Kiwhan Song, Mohamed Ali Dhraief, Muhua Xu, Locke Cai, Xuhao Chen, Arvind, and Jie Chen

In Proceedings of the Fifth ACM International Conference on AI in Finance (ICAIF), 2024

PDF Bib DOI

@inproceedings{revtrack,
  author = {Song, Kiwhan and Dhraief, Mohamed Ali and Xu, Muhua and Cai, Locke and Chen, Xuhao and Arvind and Chen, Jie},
  title = {Identifying Money Laundering Subgraphs on the Blockchain},
  booktitle = {Proceedings of the Fifth ACM International Conference on AI in Finance (ICAIF)},
  year = {2024},
  doi = {10.1145/3677052.3698635}
}

Application

The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset

Claudio Bellei, Muhua Xu, Ross Phillips, Tom Robinson, Mark Weber, Tim Kaler, Charles E. Leiserson, Arvind, and Jie Chen

In KDD Workshop on Machine Learning in Finance (KDD-W), 2024

PDF Bib WORKSHOP Dataset Code

@inproceedings{elliptic.subgraph,
  author = {Bellei, Claudio and Xu, Muhua and Phillips, Ross and Robinson, Tom and Weber, Mark and Kaler, Tim and Leiserson, Charles E. and Arvind and Chen, Jie},
  title = {The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the {Elliptic2} Dataset},
  booktitle = {KDD Workshop on Machine Learning in Finance (KDD-W)},
  year = {2024},
}

ML/AI

Representing Molecules as Random Walks Over Interpretable Grammars

Michael Sun, Minghao Guo, Weize Yuan, Veronika Thost, Crystal Elaine Owens, Aristotle Franklin Grosz, Sharvaa Selvan, Katelyn Zhou, Hassan Mohiuddin, Benjamin J. Pedretti, Zachary P. Smith, Jie Chen, and Wojciech Matusik

In Proceedings of the Forty-first International Conference on Machine Learning (ICML), 2024

PDF Bib PROCEEDINGS

@inproceedings{random.walk,
  author = {Sun, Michael and Guo, Minghao and Yuan, Weize and Thost, Veronika and Owens, Crystal Elaine and Grosz, Aristotle Franklin and Selvan, Sharvaa and Zhou, Katelyn and Mohiuddin, Hassan and Pedretti, Benjamin J. and Smith, Zachary P. and Chen, Jie and Matusik, Wojciech},
  title = {Representing Molecules as Random Walks Over Interpretable Grammars},
  booktitle = {Proceedings of the Forty-first International Conference on Machine Learning (ICML)},
  year = {2024},
}

Optimization

Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints

Yunsheng Tian, Ane Zuniga, Xinwei Zhang, Johannes P. Dürholt, Payel Das, Jie Chen, Wojciech Matusik, and Mina Konaković Luković

In Proceedings of the Forty-first International Conference on Machine Learning (ICML), 2024

PDF Bib PROCEEDINGS

@inproceedings{BO.boundary,
  author = {Tian, Yunsheng and Zuniga, Ane and Zhang, Xinwei and D\"{u}rholt, Johannes P. and Das, Payel and Chen, Jie and Matusik, Wojciech and Lukovi\'{c}, Mina Konakovi\'{c}},
  title = {Boundary Exploration for {B}ayesian Optimization With Unknown Physical Constraints},
  booktitle = {Proceedings of the Forty-first International Conference on Machine Learning (ICML)},
  year = {2024},
}

Application

Literature Mining with Large Language Models to Assist the Development of Sustainable Building Materials

Yifei Duan, Yixi Tian, Soumya Ghosh, Richard T. Goodwin, Vineeth Venugopal, Jeremy R. Gregory, Jie Chen, and Elsa A. Olivetti

In ICLR 2024 Workshop on Tackling Climate Change with Machine Learning (ICLR-W), 2024

PDF Bib WORKSHOP

@inproceedings{llm.building.material,
  author = {Duan, Yifei and Tian, Yixi and Ghosh, Soumya and Goodwin, Richard T. and Venugopal, Vineeth and Gregory, Jeremy R. and Chen, Jie and Olivetti, Elsa A.},
  title = {Literature Mining with Large Language Models to Assist the Development of Sustainable Building Materials},
  booktitle = {ICLR 2024 Workshop on Tackling Climate Change with Machine Learning (ICLR-W)},
  year = {2024},
}

ML/AI

GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data

Xinwei Zhang, Mingyi Hong, and Jie Chen

In Transactions on Machine Learning Research (TMLR), 2024

PDF Bib PROCEEDINGS

@inproceedings{glasu,
  author = {Zhang, Xinwei and Hong, Mingyi and Chen, Jie},
  title = {{GLASU}: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data},
  booktitle = {Transactions on Machine Learning Research (TMLR)},
  year = {2024},
}

Application

Bayesian Design of Concrete with Amortized Gaussian Processes and Multi-Objective Optimization

Olivia P. Pfeiffer, Kai Gong, Kristen A. Severson, Jie Chen, Jeremy R. Gregory, Soumya Ghosh, Richard T. Goodwin, and Elsa A. Olivetti

Cement and Concrete Research (CCR), 177:107406, 2024

PDF Bib DOI

@article{concrete.agp.ccr,
  author = {Pfeiffer, Olivia P. and Gong, Kai and Severson, Kristen A. and Chen, Jie and Gregory, Jeremy R. and Ghosh, Soumya and Goodwin, Richard T. and Olivetti, Elsa A.},
  title = {Bayesian Design of Concrete with Amortized {G}aussian Processes and Multi-Objective Optimization},
  journal = {Cement and Concrete Research (CCR)},
  year = {2024},
  volume = {177},
  pages = {107406},
  doi = {10.1016/j.cemconres.2023.107406}
}

ML/AI

Structured Code Representations Enable Data-Efficient Adaptation of Code Language Models

Mayank Agarwal, Yikang Shen, Bailin Wang, Yoon Kim, and Jie Chen

Preprint arXiv:2401.10716, 2024

arXiv Bib

@misc{ai4code.structured,
  author = {Agarwal, Mayank and Shen, Yikang and Wang, Bailin and Kim, Yoon and Chen, Jie},
  title = {Structured Code Representations Enable Data-Efficient Adaptation of Code Language Models},
  howpublished = {Preprint arXiv:2401.10716},
  year = {2024},
}

2023

ML/AI

Explain-Then-Translate: An Analysis on Improving Program Translation With Self-Generated Explanations

Zilu Tang, Mayank Agarwal, Alexander Shypula, Bailin Wang, Derry Wijaya, Jie Chen, and Yoon Kim

In Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings), 2023

PDF Bib DOI

@inproceedings{explain.then.translate,
  author = {Tang, Zilu and Agarwal, Mayank and Shypula, Alexander and Wang, Bailin and Wijaya, Derry and Chen, Jie and Kim, Yoon},
  title = {Explain-Then-Translate: An Analysis on Improving Program Translation With Self-Generated Explanations},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings)},
  year = {2023},
  doi = {10.18653/v1/2023.findings-emnlp.119}
}

ML/AI

Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs

Tengfei Ma, Trong Nghia Hoang, and Jie Chen

In Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI), 2023

PDF Bib PROCEEDINGS

@inproceedings{f3,
  author = {Ma, Tengfei and Hoang, Trong Nghia and Chen, Jie},
  title = {Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs},
  booktitle = {Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI)},
  year = {2023},
}

ML/AI

A Gromov–Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening

Yifan Chen, Rentian Yao, Yun Yang, and Jie Chen

In Proceedings of the Fortieth International Conference on Machine Learning (ICML), 2023

PDF Bib PROCEEDINGS Slides

@inproceedings{gw.coarsening,
  author = {Chen, Yifan and Yao, Rentian and Yang, Yun and Chen, Jie},
  title = {A {G}romov--{W}asserstein Geometric View of Spectrum-Preserving Graph Coarsening},
  booktitle = {Proceedings of the Fortieth International Conference on Machine Learning (ICML)},
  year = {2023},
}

ML/AI

GC-Flow: A Graph-Based Flow Network for Effective Clustering

Tianchun Wang, Farzaneh Mirzazadeh, Xiang Zhang, and Jie Chen

In Proceedings of the Fortieth International Conference on Machine Learning (ICML), 2023

PDF Bib PROCEEDINGS Poster

@inproceedings{gc.flow,
  author = {Wang, Tianchun and Mirzazadeh, Farzaneh and Zhang, Xiang and Chen, Jie},
  title = {{GC-Flow}: A Graph-Based Flow Network for Effective Clustering},
  booktitle = {Proceedings of the Fortieth International Conference on Machine Learning (ICML)},
  year = {2023},
}

ML/AI

Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction

Minghao Guo, Veronika Thost, Samuel W. Song, Adithya Balachandran, Payel Das, Jie Chen, and Wojciech Matusik

In Proceedings of the Fortieth International Conference on Machine Learning (ICML), 2023

PDF Bib PROCEEDINGS Media

@inproceedings{geo.deg,
  author = {Guo, Minghao and Thost, Veronika and Song, Samuel W. and Balachandran, Adithya and Das, Payel and Chen, Jie and Matusik, Wojciech},
  title = {Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction},
  booktitle = {Proceedings of the Fortieth International Conference on Machine Learning (ICML)},
  year = {2023},
}

Optimization

Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data

Yonggui Yan, Jie Chen, Pin-Yu Chen, Xiaodong Cui, Songtao Lu, and Yangyang Xu

In Proceedings of the Fortieth International Conference on Machine Learning (ICML), 2023

PDF Bib PROCEEDINGS

@inproceedings{dproxsgt,
  author = {Yan, Yonggui and Chen, Jie and Chen, Pin-Yu and Cui, Xiaodong and Lu, Songtao and Xu, Yangyang},
  title = {Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data},
  booktitle = {Proceedings of the Fortieth International Conference on Machine Learning (ICML)},
  year = {2023},
}

Scientific Computing

Bridging Mean-Field Games and Normalizing Flows with Trajectory Regularization

Han Huang, Jiajia Yu, Jie Chen, and Rongjie Lai

Journal of Computational Physics (JCP), 487:112155, 2023

PDF Bib DOI

@article{mfg.nf,
  author = {Huang, Han and Yu, Jiajia and Chen, Jie and Lai, Rongjie},
  title = {Bridging Mean-Field Games and Normalizing Flows with Trajectory Regularization},
  journal = {Journal of Computational Physics (JCP)},
  volume = {487},
  pages = {112155},
  year = {2023},
  doi = {10.1016/j.jcp.2023.112155}
}

Optimization

Parallel and Distributed Asynchronous Adaptive Stochastic Gradient Methods

Yangyang Xu, Yibo Xu, Yonggui Yan, Colin Sutcher-Shepard, Leopold Grinberg, and Jie Chen

Mathematical Programming Computation (MPC), 15:471–508, 2023

PDF Bib DOI

@article{apam,
  author = {Xu, Yangyang and Xu, Yibo and Yan, Yonggui and Sutcher-Shepard, Colin and Grinberg, Leopold and Chen, Jie},
  title = {Parallel and Distributed Asynchronous Adaptive Stochastic Gradient Methods},
  journal = {Mathematical Programming Computation (MPC)},
  volume = {15},
  pages = {471--508},
  year = {2023},
  doi = {10.1007/s12532-023-00237-5}
}

System

Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching

Tim Kaler, Alexandros-Stavros Iliopoulos, Philip Murzynowski, Tao B. Schardl, Charles E. Leiserson, and Jie Chen

In Proceedings of Machine Learning and Systems 5 (MLSys), 2023

PDF Bib PROCEEDINGS Code

@inproceedings{salient++,
  author = {Kaler, Tim and Iliopoulos, Alexandros-Stavros and Murzynowski, Philip and Schardl, Tao B. and Leiserson, Charles E. and Chen, Jie},
  title = {Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching},
  booktitle = {Proceedings of Machine Learning and Systems 5 (MLSys)},
  year = {2023},
}

ML/AI

Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning

Zehao Niu, Mihai Anitescu, and Jie Chen

In Proceedings of the Eleventh International Conference on Learning Representations (ICLR), 2023

PDF Bib PROCEEDINGS

@inproceedings{gnngp,
  author = {Niu, Zehao and Anitescu, Mihai and Chen, Jie},
  title = {Graph Neural Network-Inspired Kernels for {G}aussian Processes in Semi-Supervised Learning},
  booktitle = {Proceedings of the Eleventh International Conference on Learning Representations (ICLR)},
  year = {2023},
}

Optimization

A Decentralized Primal-Dual Framework for Non-convex Smooth Consensus Optimization

Gabriel Mancino-Ball, Yangyang Xu, and Jie Chen

IEEE Transactions on Signal Processing (TSP), 71:525–538, 2023

PDF Bib DOI

@article{adapd,
  author = {Mancino-Ball, Gabriel and Xu, Yangyang and Chen, Jie},
  title = {A Decentralized Primal-Dual Framework for Non-convex Smooth Consensus Optimization},
  journal = {IEEE Transactions on Signal Processing (TSP)},
  year = {2023},
  volume = {71},
  pages = {525--538},
  doi = {10.1109/TSP.2023.3239799}
}

Optimization

Proximal Stochastic Recursive Momentum Methods for Nonconvex Composite Decentralized Optimization

Gabriel Mancino-Ball, Shengnan Miao, Yangyang Xu, and Jie Chen

In Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023

PDF Bib DOI

@inproceedings{deepstorm,
  author = {Mancino-Ball, Gabriel and Miao, Shengnan and Xu, Yangyang and Chen, Jie},
  title = {Proximal Stochastic Recursive Momentum Methods for Nonconvex Composite Decentralized Optimization},
  booktitle = {Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  doi = {10.1609/aaai.v37i7.26087}
}

Statistics

Linear-Cost Covariance Functions for Gaussian Random Fields

Jie Chen and Michael L. Stein

Journal of the American Statistical Association (JASA), 118(541):147–164, 2023

PDF Bib DOI Code Slides

@article{linear.covar.func,
  author = {Chen, Jie and Stein, Michael L.},
  title = {Linear-Cost Covariance Functions for {G}aussian Random Fields},
  journal = {Journal of the American Statistical Association (JASA)},
  year = {2023},
  volume = {118},
  number = {541},
  pages = {147--164},
  doi = {10.1080/01621459.2021.1919122},
}

2022

Scientific Computing

Graph Neural Networks for Selection of Preconditioners and Krylov Solvers

Ziyuan Tang, Hong Zhang, and Jie Chen

In NeurIPS 2022 New Frontiers in Graph Learning Workshop (NeurIPS-W), 2022

PDF Bib WORKSHOP

@inproceedings{gnn.preconditioner,
  author = {Tang, Ziyuan and Zhang, Hong and Chen, Jie},
  title = {Graph Neural Networks for Selection of Preconditioners and {K}rylov Solvers},
  booktitle = {NeurIPS 2022 New Frontiers in Graph Learning Workshop (NeurIPS-W)},
  year = {2022},
}

ML/AI

Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series

Enyan Dai and Jie Chen

In Proceedings of the Tenth International Conference on Learning Representations (ICLR), 2022

PDF Bib PROCEEDINGS Slides Media

@inproceedings{ganf,
  author = {Dai, Enyan and Chen, Jie},
  title = {Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series},
  booktitle = {Proceedings of the Tenth International Conference on Learning Representations (ICLR)},
  year = {2022},
}

ML/AI

Data-Efficient Graph Grammar Learning for Molecular Generation

Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, and Wojciech Matusik

In Proceedings of the Tenth International Conference on Learning Representations (ICLR), 2022

PDF Bib PROCEEDINGS Video Media

@inproceedings{deg,
  author = {Guo, Minghao and Thost, Veronika and Li, Beichen and Das, Payel and Chen, Jie and Matusik, Wojciech},
  title = {Data-Efficient Graph Grammar Learning for Molecular Generation},
  booktitle = {Proceedings of the Tenth International Conference on Learning Representations (ICLR)},
  year = {2022},
}

System

Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining

Tim Kaler, Nickolas Stathas, Anne Ouyang, Alexandros-Stavros Iliopoulos, Tao B. Schardl, Charles E. Leiserson, and Jie Chen

In Proceedings of Machine Learning and Systems 4 (MLSys), 2022

PDF Bib PROCEEDINGS Code Media

@inproceedings{salient,
  author = {Kaler, Tim and Stathas, Nickolas and Ouyang, Anne and Iliopoulos, Alexandros-Stavros and Schardl, Tao B. and Leiserson, Charles E. and Chen, Jie},
  title = {Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining},
  booktitle = {Proceedings of Machine Learning and Systems 4 (MLSys)},
  year = {2022},
}

Optimization

Distributed Stochastic Inertial-Accelerated Methods with Delayed Derivatives for Nonconvex Problems

Yangyang Xu, Yibo Xu, Yonggui Yan, and Jie Chen

SIAM Journal on Imaging Sciences (SIIMS), 15(2):550–590, 2022

PDF Bib DOI

@article{heavyball,
  author = {Xu, Yangyang and Xu, Yibo and Yan, Yonggui and Chen, Jie},
  title = {Distributed Stochastic Inertial-Accelerated Methods with Delayed Derivatives for Nonconvex Problems},
  journal = {SIAM Journal on Imaging Sciences (SIIMS)},
  year = {2022},
  volume = {15},
  number = {2},
  pages = {550--590},
  doi = {10.1137/21M1435719}
}

Data Science

Graph Coarsening: From Scientific Computing to Machine Learning

Jie Chen, Yousef Saad, and Zechen Zhang

SeMA Journal: Bulletin of the Spanish Society of Applied Mathematics (SeMA), 79(1):187–223, 2022

PDF Bib DOI

@article{coarsening.sc.ml,
  author = {Chen, Jie and Saad, Yousef and Zhang, Zechen},
  title = {Graph Coarsening: From Scientific Computing to Machine Learning},
  journal = {SeMA Journal: Bulletin of the Spanish Society of Applied Mathematics (SeMA)},
  year = {2022},
  volume = {79},
  number = {1},
  pages = {187--223},
  doi = {10.1007/s40324-021-00282-x}
}

2021

Application

Amortized Inference of Gaussian Process Hyperparameters for Improved Concrete Strength Trajectory Prediction

Kristen A. Severson, Olivia P. Pfeiffer, Jie Chen, Kai Gong, Jeremy Gregory, Richard Goodwin, and Elsa A. Olivetti

In NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning (NeurIPS-W), 2021

PDF Bib WORKSHOP

@inproceedings{concrete.agp,
  author = {Severson, Kristen A. and Pfeiffer, Olivia P. and Chen, Jie and Gong, Kai and Gregory, Jeremy and Goodwin, Richard and Olivetti, Elsa A.},
  title = {Amortized Inference of {G}aussian Process Hyperparameters for Improved Concrete Strength Trajectory Prediction},
  booktitle = {NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning (NeurIPS-W)},
  year = {2021},
}

ML/AI

Project CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks

Ruchir Puri, David S. Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladimir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, and Frederick Reiss

In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS), 2021

PDF Bib PROCEEDINGS

@inproceedings{codenet,
  author = {Puri, Ruchir and Kung, David S. and Janssen, Geert and Zhang, Wei and Domeniconi, Giacomo and Zolotov, Vladimir and Dolby, Julian and Chen, Jie and Choudhury, Mihir and Decker, Lindsey and Thost, Veronika and Buratti, Luca and Pujar, Saurabh and Ramji, Shyam and Finkler, Ulrich and Malaika, Susan and Reiss, Frederick},
  title = {Project {CodeNet}: A Large-Scale {AI} for Code Dataset for Learning a Diversity of Coding Tasks},
  booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS)},
  year = {2021},
}

ML/AI

Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models

Xiao Zang, Yi Xie, Jie Chen, and Bo Yuan

In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI), 2021

PDF Bib DOI

@inproceedings{gua,
  author = {Zang, Xiao and Xie, Yi and Chen, Jie and Yuan, Bo},
  title = {Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI)},
  year = {2021},
  doi = {10.24963/ijcai.2021/458}
}

ML/AI

Discrete Graph Structure Learning for Forecasting Multiple Time Series

Chao Shang, Jie Chen, and Jinbo Bi

In Proceedings of the Ninth International Conference on Learning Representations (ICLR), 2021

PDF Bib PROCEEDINGS Poster

@inproceedings{gts,
  author = {Shang, Chao and Chen, Jie and Bi, Jinbo},
  title = {Discrete Graph Structure Learning for Forecasting Multiple Time Series},
  booktitle = {Proceedings of the Ninth International Conference on Learning Representations (ICLR)},
  year = {2021},
}

ML/AI

Directed Acyclic Graph Neural Networks

Veronika Thost and Jie Chen

In Proceedings of the Ninth International Conference on Learning Representations (ICLR), 2021

PDF Bib PROCEEDINGS

@inproceedings{dagnn,
  author = {Thost, Veronika and Chen, Jie},
  title = {Directed Acyclic Graph Neural Networks},
  booktitle = {Proceedings of the Ninth International Conference on Learning Representations (ICLR)},
  year = {2021},
}

ML/AI

Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport

Tengfei Ma and Jie Chen

In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021

PDF Bib DOI Slides

@inproceedings{otcoarsening,
  author = {Ma, Tengfei and Chen, Jie},
  title = {Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport},
  booktitle = {Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  doi = {10.1609/aaai.v35i10.17072},
}

Parallel Computing

PARAD: A Work-Efficient Parallel Algorithm for Reverse-Mode Automatic Differentiation

Tim Kaler, Tao B. Schardl, Brian Xie, Charles E. Leiserson, Jie Chen, Aldo Pareja, and Georgios Kollias

In Symposium on Algorithmic Principles of Computer Systems (APOCS), 2021

PDF Bib DOI

@inproceedings{parad,
  author = {Kaler, Tim and Schardl, Tao B. and Xie, Brian and Leiserson, Charles E. and Chen, Jie and Pareja, Aldo and Kollias, Georgios},
  title = {{PARAD}: A Work-Efficient Parallel Algorithm for Reverse-Mode Automatic Differentiation},
  booktitle = {Symposium on Algorithmic Principles of Computer Systems (APOCS)},
  year = {2021},
  doi = {10.1137/1.9781611976489.11}
}

ML/AI

Generating a Doppelganger Graph: Resembling but Distinct

Yuliang Ji, Ru Huang, Jie Chen, and Yuanzhe Xi

Preprint arXiv:2101.09593, 2021

arXiv Bib

@misc{doppelganger,
  author = {Ji, Yuliang and Huang, Ru and Chen, Jie and Xi, Yuanzhe},
  title = {Generating a Doppelganger Graph: Resembling but Distinct},
  howpublished = {Preprint arXiv:2101.09593},
  year = {2021},
}

2020

ML/AI

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao B. Schardl, and Charles E. Leiserson

In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020

PDF Bib DOI Poster

@inproceedings{evolvegcn,
  author = {Pareja, Aldo and Domeniconi, Giacomo and Chen, Jie and Ma, Tengfei and Suzumura, Toyotaro and Kanezashi, Hiroki and Kaler, Tim and Schardl, Tao B. and Leiserson, Charles E.},
  title = {{EvolveGCN}: Evolving Graph Convolutional Networks for Dynamic Graphs},
  booktitle = {Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  doi = {10.1609/aaai.v34i04.5984},
}

ML/AI

Embedding Compression with Isotropic Iterative Quantization

Siyu Liao, Jie Chen, Yanzhi Wang, Qinru Qiu, and Bo Yuan

In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020

PDF Bib DOI

@inproceedings{embedding.compression,
  author = {Liao, Siyu and Chen, Jie and Wang, Yanzhi and Qiu, Qinru and Yuan, Bo},
  title = {Embedding Compression with Isotropic Iterative Quantization},
  booktitle = {Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  doi = {10.1609/aaai.v34i05.6350}
}

ML/AI

CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator

Huy Phan, Yi Xie, Siyu Liao, Jie Chen, and Bo Yuan

In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020

PDF Bib DOI

@inproceedings{cag,
  author = {Phan, Huy and Xie, Yi and Liao, Siyu and Chen, Jie and Yuan, Bo},
  title = {{CAG}: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator},
  booktitle = {Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  doi = {10.1609/aaai.v34i04.5990}
}

ML/AI

Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Tengfei Ma, Patrick Ferber, Siyu Huo, Jie Chen, and Michael Katz

In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020

PDF Bib DOI Slides

@inproceedings{planning.graph,
  author = {Ma, Tengfei and Ferber, Patrick and Huo, Siyu and Chen, Jie and Katz, Michael},
  title = {Online Planner Selection with Graph Neural Networks and Adaptive Scheduling},
  booktitle = {Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  doi = {10.1609/aaai.v34i04.5949},
}

2019

Application

Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics

Mark Weber, Giacomo Domeniconi, Jie Chen, Daniel Karl I. Weidele, Claudio Bellei, Tom Robinson, and Charles E. Leiserson

In 2nd KDD Workshop on Anomaly Detection in Finance (KDD-W), 2019

PDF Bib WORKSHOP Dataset

@inproceedings{graph.aml.gcn,
  author = {Weber, Mark and Domeniconi, Giacomo and Chen, Jie and Weidele, Daniel Karl I. and Bellei, Claudio and Robinson, Tom and Leiserson, Charles E.},
  title = {Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics},
  booktitle = {2nd KDD Workshop on Anomaly Detection in Finance (KDD-W)},
  year = {2019},
}

ML/AI

IPC: A Benchmark Data Set for Learning with Graph-Structured Data

Patrick Ferber, Tengfei Ma, Siyu Huo, Jie Chen, and Michael Katz

In ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data (ICML-W), 2019

PDF Bib WORKSHOP Dataset Poster

@inproceedings{ipc.graph.data,
  author = {Ferber, Patrick and Ma, Tengfei and Huo, Siyu and Chen, Jie and Katz, Michael},
  title = {{IPC}: A Benchmark Data Set for Learning with Graph-Structured Data},
  booktitle = {ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data (ICML-W)},
  year = {2019},
}

ML/AI

DAG-GNN: DAG Structure Learning with Graph Neural Networks

Yue Yu, Jie Chen, Tian Gao, and Mo Yu

In Proceedings of the Thirty-sixth International Conference on Machine Learning (ICML), 2019

PDF Bib PROCEEDINGS Slides

@inproceedings{dag.gnn,
  author = {Yu, Yue and Chen, Jie and Gao, Tian and Yu, Mo},
  title = {{DAG-GNN}: {DAG} Structure Learning with Graph Neural Networks},
  booktitle = {Proceedings of the Thirty-sixth International Conference on Machine Learning (ICML)},
  year = {2019},
}

Scientific Computing

Relaxation-Based Coarsening for Multilevel Hypergraph Partitioning

Ruslan Shaydulin, Jie Chen, and Ilya Safro

Multiscale Modeling & Simulation (MMS), 17(1):482–506, 2019

PDF Bib DOI

@article{alge.dist.hypergraph,
  author = {Shaydulin, Ruslan and Chen, Jie and Safro, Ilya},
  title = {Relaxation-Based Coarsening for Multilevel Hypergraph Partitioning},
  journal = {Multiscale Modeling \& Simulation (MMS)},
  year = {2019},
  volume = {17},
  number = {1},
  pages = {482--506},
  doi = {http://dx.doi.org/10.1137/17M1152735}
}

ML/AI

A Sequential Set Generation Method for Predicting Set-Valued Outputs

Tian Gao, Jie Chen, Vijil Chenthamarakshan, and Michael Witbrock

In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019

PDF Bib DOI

@inproceedings{set.output,
  author = {Gao, Tian and Chen, Jie and Chenthamarakshan, Vijil and Witbrock, Michael},
  title = {A Sequential Set Generation Method for Predicting Set-Valued Outputs},
  booktitle = {Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  doi = {10.1609/aaai.v33i01.33012835}
}

ML/AI

Chart Auto-Encoders for Manifold Structured Data

Stefan Schonsheck, Jie Chen, and Rongjie Lai

Preprint arXiv:1912.10094, 2019

arXiv Bib

@misc{cae,
  author = {Schonsheck, Stefan and Chen, Jie and Lai, Rongjie},
  title = {Chart Auto-Encoders for Manifold Structured Data},
  howpublished = {Preprint arXiv:1912.10094},
  year = {2019},
}

2018

Application

Scalable Graph Learning for Anti-Money Laundering: A First Look

Mark Weber, Jie Chen, Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E. Leiserson, and Tao B. Schardl

In NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy (NIPS-W), 2018

PDF Bib WORKSHOP

@inproceedings{graph.aml,
  author = {Weber, Mark and Chen, Jie and Suzumura, Toyotaro and Pareja, Aldo and Ma, Tengfei and Kanezashi, Hiroki and Kaler, Tim and Leiserson, Charles E. and Schardl, Tao B.},
  title = {Scalable Graph Learning for Anti-Money Laundering: A First Look},
  booktitle = {NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy (NIPS-W)},
  year = {2018},
}

ML/AI

Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders

Tengfei Ma, Jie Chen, and Cao Xiao

In Advances in Neural Information Processing Systems 31 (NeurIPS), 2018

PDF Bib PROCEEDINGS Poster

@inproceedings{graph.generation,
  author = {Ma, Tengfei and Chen, Jie and Xiao, Cao},
  title = {Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders},
  booktitle = {Advances in Neural Information Processing Systems 31 (NeurIPS)},
  year = {2018},
}

Optimization

Learning Low-Complexity Autoregressive Models via Proximal Alternating Minimization

Fu Lin and Jie Chen

Systems & Control Letters (SCL), 122:48–53, 2018

PDF Bib DOI

@article{palm.full,
  author = {Lin, Fu and Chen, Jie},
  title = {Learning Low-Complexity Autoregressive Models via Proximal Alternating Minimization},
  journal = {Systems \& Control Letters (SCL)},
  year = {2018},
  volume = {122},
  pages = {48--53},
  doi = {10.1016/j.sysconle.2018.09.009}
}

Scientific Computing

A Posteriori Error Estimate for Computing tr(f(A)) by Using the Lanczos Method

Jie Chen and Yousef Saad

Numerical Linear Algebra with Applications (NLAA), 25(5):e2170, 2018

PDF Bib DOI

@article{trfA,
  author = {Chen, Jie and Saad, Yousef},
  title = {A Posteriori Error Estimate for Computing {$tr(f(A))$} by Using the {L}anczos Method},
  journal = {Numerical Linear Algebra with Applications (NLAA)},
  year = {2018},
  volume = {25},
  number = {5},
  pages = {e2170},
  doi = {10.1002/nla.2170}
}

ML/AI

FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling

Jie Chen, Tengfei Ma, and Cao Xiao

In Proceedings of the Sixth International Conference on Learning Representations (ICLR), 2018

PDF Bib PROCEEDINGS Code Poster

@inproceedings{fast.gcn,
  author = {Chen, Jie and Ma, Tengfei and Xiao, Cao},
  title = {{FastGCN}: Fast Learning with Graph Convolutional Networks via Importance Sampling},
  booktitle = {Proceedings of the Sixth International Conference on Learning Representations (ICLR)},
  year = {2018},
}

ML/AI

Stochastic Gradient Descent with Biased but Consistent Gradient Estimators

Jie Chen and Ronny Luss

Preprint arXiv:1807.11880, 2018

arXiv Bib Code

@misc{sgd.consistent,
  author = {Chen, Jie and Luss, Ronny},
  title = {Stochastic Gradient Descent with Biased but Consistent Gradient Estimators},
  howpublished = {Preprint arXiv:1807.11880},
  year = {2018},
}

2017

Statistics

Hierarchically Compositional Kernels for Scalable Nonparametric Learning

Jie Chen, Haim Avron, and Vikas Sindhwani

Journal of Machine Learning Research (JMLR), 18(66):1–42, 2017

PDF Bib JOURNAL

@article{hierarchical,
  author = {Chen, Jie and Avron, Haim and Sindhwani, Vikas},
  title = {Hierarchically Compositional Kernels for Scalable Nonparametric Learning},
  journal = {Journal of Machine Learning Research (JMLR)},
  year = {2017},
  volume = {18},
  number = {66},
  pages = {1--42},
}

Scientific Computing

Fast Estimation of tr(f(A)) via Stochastic Lanczos Quadrature

Shashanka Ubaru, Jie Chen, and Yousef Saad

SIAM Journal on Matrix Analysis and Applications (SIMAX), 38(4):1075–1099, 2017

PDF Bib DOI

@article{lanc.quad,
  author = {Ubaru, Shashanka and Chen, Jie and Saad, Yousef},
  title = {Fast Estimation of {$tr(f(A))$} via Stochastic {L}anczos Quadrature},
  journal = {SIAM Journal on Matrix Analysis and Applications (SIMAX)},
  year = {2017},
  volume = {38},
  number = {4},
  pages = {1075--1099},
  doi = {10.1137/16M1104974}
}

Optimization

Learning Low-Complexity Autoregressive Models with Limited Time Sequence Data

Fu Lin and Jie Chen

In "Proceedings of the 2017 American Control Conference" # "acc", 2017

PDF Bib DOI

@inproceedings{palm,
  author = {Lin, Fu and Chen, Jie},
  title = {Learning Low-Complexity Autoregressive Models with Limited Time Sequence Data},
  booktitle = "Proceedings of the 2017 American Control Conference" # acc,
  year = {2017},
  doi = {10.23919/ACC.2017.7963432},
}

Statistics

An Inversion-Free Estimating Equations Approach for Gaussian Process Models

Mihai Anitescu, Jie Chen, and Michael L. Stein

Journal of Computational and Graphical Statistics (JCGS), 26(1):98–107, 2017

PDF Bib DOI

@article{esteq,
  author = {Anitescu, Mihai and Chen, Jie and Stein, Michael L.},
  title = {An Inversion-Free Estimating Equations Approach for {G}aussian Process Models},
  journal = {Journal of Computational and Graphical Statistics (JCGS)},
  year = {2017},
  volume = {26},
  number = {1},
  pages = {98--107},
  doi = {10.1080/10618600.2016.1164056}
}

2016

Scientific Computing

How Accurately Should I Compute Implicit Matrix-Vector Products When Applying the Hutchinson Trace Estimator?

Jie Chen

SIAM Journal on Scientific Computing (SISC), 38(6):A3515–A3539, 2016

PDF Bib DOI

@article{hutchinson,
  author = {Chen, Jie},
  title = {How Accurately Should {I} Compute Implicit Matrix-Vector Products When Applying the {H}utchinson Trace Estimator?},
  journal = {SIAM Journal on Scientific Computing (SISC)},
  year = {2016},
  volume = {38},
  number = {6},
  pages = {A3515--A3539},
  doi = {10.1137/15M1051506}
}

ML/AI

Revisiting Random Binning Feature: Fast Convergence and Strong Parallelizability

Lingfei Wu, Ian E.H. Yen, Jie Chen, and Rui Yan

In Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016

PDF Bib DOI

@inproceedings{random.binning,
  author = {Wu, Lingfei and Yen, Ian E.H. and Chen, Jie and Yan, Rui},
  title = {Revisiting Random Binning Feature: Fast Convergence and Strong Parallelizability},
  booktitle = {Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)},
  year = {2016},
  doi = {10.1145/2939672.2939794}
}

Scientific Computing

Analysis and Practical Use of Flexible BiCGStab

Jie Chen, Lois Curfman McInnes, and Hong Zhang

Journal of Scientific Computing (JSC), 68(2):803–825, 2016

PDF Bib DOI

@article{fbcgs,
  author = {Chen, Jie and McInnes, Lois Curfman and Zhang, Hong},
  title = {Analysis and Practical Use of Flexible {BiCGStab}},
  journal = {Journal of Scientific Computing (JSC)},
  year = {2016},
  volume = {68},
  number = {2},
  pages = {803--825},
  doi = {10.1007/s10915-015-0159-4}
}

ML/AI

Efficient One-Vs-One Kernel Ridge Regression for Speech Recognition

Jie Chen, Lingfei Wu, Kartik Audhkhasi, Brian Kingsbury, and Bhuvana Ramabhadran

In Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016

PDF Bib DOI

@inproceedings{onevsone,
  author = {Chen, Jie and Wu, Lingfei and Audhkhasi, Kartik and Kingsbury, Brian and Ramabhadran, Bhuvana},
  title = {Efficient One-Vs-One Kernel Ridge Regression for Speech Recognition},
  booktitle = {Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year = {2016},
  doi = {10.1109/ICASSP.2016.7472118}
}

Statistics

On Bochner’s and Polya’s Characterizations of Positive-Definite Kernels and the Respective Random Feature Maps

Jie Chen, Dehua Cheng, and Yan Liu

Preprint arXiv:1610.08861, 2016

arXiv Bib

@misc{krr.rb,
  author = {Chen, Jie and Cheng, Dehua and Liu, Yan},
  title = {On {B}ochner's and {P}olya's Characterizations of Positive-Definite Kernels and the Respective Random Feature Maps},
  howpublished = {Preprint arXiv:1610.08861},
  year = {2016},
}

2015

Optimization

Scalable Computation of Regularized Precision Matrices via Stochastic Optimization

Yves F. Atchadé, Rahul Mazumder, and Jie Chen

Technical Report RC25543, IBM Thomas J. Watson Research Center, 2015

PDF Bib

@techreport{precision,
  author = {Atchad\'{e}, Yves F. and Mazumder, Rahul and Chen, Jie},
  title = {Scalable Computation of Regularized Precision Matrices via Stochastic Optimization},
  institution = {IBM Thomas J.\ Watson Research Center},
  year = {2015},
  number = {RC25543},
}

2014

Parallel Computing

A Parallel Linear Solver for Multilevel Toeplitz Systems with Possibly Several Right-Hand Sides

Jie Chen, Tom L. H. Li, and Mihai Anitescu

Parallel Computing (PARCO), 40(8):408–424, 2014

PDF Bib DOI

@article{cg.fft.journal,
  author = {Chen, Jie and Li, Tom L. H. and Anitescu, Mihai},
  title = {A Parallel Linear Solver for Multilevel {T}oeplitz Systems with Possibly Several Right-Hand Sides},
  journal = {Parallel Computing (PARCO)},
  year = {2014},
  volume = {40},
  number = {8},
  pages = {408--424},
  doi = {10.1016/j.parco.2014.06.004}
}

Scientific Computing

A Fast Summation Tree Code for Matérn Kernel

Jie Chen, Lei Wang, and Mihai Anitescu

SIAM Journal on Scientific Computing (SISC), 36(1):A289–A309, 2014

PDF Bib DOI

@article{matern.treecode,
  author = {Chen, Jie and Wang, Lei and Anitescu, Mihai},
  title = {A Fast Summation Tree Code for {M}at\'{e}rn Kernel},
  journal = {SIAM Journal on Scientific Computing (SISC)},
  year = {2014},
  volume = {36},
  number = {1},
  pages = {A289--A309},
  doi = {10.1137/120903002}
}

Scientific Computing

Computing Square Root Factorization for Recursively Low-Rank Compressed Matrices

Jie Chen

Technical Report RC25499, IBM Thomas J. Watson Research Center, 2014

PDF Bib

@techreport{sqrt,
  author = {Chen, Jie},
  title = {Computing Square Root Factorization for Recursively Low-Rank Compressed Matrices},
  institution = {IBM Thomas J.\ Watson Research Center},
  year = {2014},
  number = {RC25499},
}

Scientific Computing

Data Structure and Algorithms for Recursively Low-Rank Compressed Matrices

Jie Chen

Technical Report ANL/MCS-P5112-0314, Argonne National Laboratory, 2014

PDF Bib

@techreport{inv,
  author = {Chen, Jie},
  title = {Data Structure and Algorithms for Recursively Low-Rank Compressed Matrices},
  institution = {Argonne National Laboratory},
  year = {2014},
  number = {ANL/MCS-P5112-0314},
}

Scientific Computing

A Stable Scaling of Newton-Schulz for Improving the Sign Function Computation of a Hermitian Matrix

Jie Chen and Edmond Chow

Technical Report ANL/MCS-P5059-0114, Argonne National Laboratory, 2014

PDF Bib

@techreport{sign,
  author = {Chen, Jie and Chow, Edmond},
  title = {A Stable Scaling of {N}ewton-{S}chulz for Improving the Sign Function Computation of a {H}ermitian Matrix},
  institution = {Argonne National Laboratory},
  year = {2014},
  number = {ANL/MCS-P5059-0114},
}

2013

Scientific Computing

On the Use of Discrete Laplace Operator for Preconditioning Kernel Matrices

Jie Chen

SIAM Journal on Scientific Computing (SISC), 35(2):A577–A602, 2013

PDF Bib DOI Slides

@article{lap.precond,
  author = {Chen, Jie},
  title = {On the Use of Discrete {L}aplace Operator for Preconditioning Kernel Matrices},
  journal = {SIAM Journal on Scientific Computing (SISC)},
  year = {2013},
  volume = {35},
  number = {2},
  pages = {A577--A602},
  doi = {10.1137/120874527},
}

Parallel Computing

Parallelizing the Conjugate Gradient Algorithm for Multilevel Toeplitz Systems

Jie Chen and Tom L. H. Li

Procedia Computer Science, 18:571–580, 2013

PDF Bib DOI Slides

@article{cg.fft,
  author = {Chen, Jie and Li, Tom L. H.},
  title = {Parallelizing the Conjugate Gradient Algorithm for Multilevel {T}oeplitz Systems},
  journal = {Procedia Computer Science},
  year = {2013},
  volume = {18},
  pages = {571--580},
  doi = {10.1016/j.procs.2013.05.221},
}

Statistics

Stochastic Approximation of Score Functions for Gaussian Processes

Michael L. Stein, Jie Chen, and Mihai Anitescu

Annals of Applied Statistics (AoAS), 7(2):1162–1191, 2013

PDF Bib DOI Code

@article{score.func,
  author = {Stein, Michael L. and Chen, Jie and Anitescu, Mihai},
  title = {Stochastic Approximation of Score Functions for {G}aussian Processes},
  journal = {Annals of Applied Statistics (AoAS)},
  year = {2013},
  volume = {7},
  number = {2},
  pages = {1162--1191},
  doi = {10.1214/13-AOAS627},
}

Parallel Computing

A Parallel Tree Code for Computing Matrix-Vector Products with the Matérn Kernel

Jie Chen, Lei Wang, and Mihai Anitescu

Technical Report ANL/MCS-P5015-0913, Argonne National Laboratory, 2013

PDF Bib Code Gallery

@techreport{matern.treecode.parallel,
  author = {Chen, Jie and Wang, Lei and Anitescu, Mihai},
  title = {A Parallel Tree Code for Computing Matrix-Vector Products with the {M}at\'{e}rn Kernel},
  institution = {Argonne National Laboratory},
  year = {2013},
  number = {ANL/MCS-P5015-0913},
}

2012

Scientific Computing

Difference Filter Preconditioning for Large Covariance Matrices

Michael L. Stein, Jie Chen, and Mihai Anitescu

SIAM Journal on Matrix Analysis and Applications (SIMAX), 33(1):52–72, 2012

PDF Bib DOI

@article{covar.cond,
  author = {Stein, Michael L. and Chen, Jie and Anitescu, Mihai},
  title = {Difference Filter Preconditioning for Large Covariance Matrices},
  journal = {SIAM Journal on Matrix Analysis and Applications (SIMAX)},
  year = {2012},
  volume = {33},
  number = {1},
  pages = {52--72},
  doi = {10.1137/110834469}
}

Scientific Computing

A Matrix-Free Approach for Solving the Parametric Gaussian Process Maximum Likelihood Problem

Mihai Anitescu, Jie Chen, and Lei Wang

SIAM Journal on Scientific Computing (SISC), 34(1):A240–A262, 2012

PDF Bib DOI Code Slides Website

@article{GP-SAA,
  author = {Anitescu, Mihai and Chen, Jie and Wang, Lei},
  title = {A Matrix-Free Approach for Solving the Parametric {G}aussian Process Maximum Likelihood Problem},
  journal = {SIAM Journal on Scientific Computing (SISC)},
  year = {2012},
  volume = {34},
  number = {1},
  pages = {A240--A262},
  doi = {10.1137/110831143},
}

Data Science

Dense Subgraph Extraction with Application to Community Detection

Jie Chen and Yousef Saad

IEEE Transactions on Knowledge and Data Engineering (TKDE), 24(7):1216–1230, 2012

PDF Bib DOI

@article{dense.subgraph,
  author = {Chen, Jie and Saad, Yousef},
  title = {Dense Subgraph Extraction with Application to Community Detection},
  journal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},
  year = {2012},
  volume = {24},
  number = {7},
  pages = {1216--1230},
  doi = {10.1109/TKDE.2010.271}
}

2011

Scientific Computing

Algebraic Distance on Graphs

Jie Chen and Ilya Safro

SIAM Journal on Scientific Computing (SISC), 33(6):3468–3490, 2011

PDF Bib DOI

@article{vertex.connectivity,
  author = {Chen, Jie and Safro, Ilya},
  title = {Algebraic Distance on Graphs},
  journal = {SIAM Journal on Scientific Computing (SISC)},
  year = {2011},
  volume = {33},
  number = {6},
  pages = {3468--3490},
  doi = {10.1137/090775087}
}

Scientific Computing

A Measure of the Local Connectivity between Graph Vertices

Jie Chen and Ilya Safro

Procedia Computer Science, 4:196–205, 2011

PDF Bib DOI

@article{alge.dist.conf,
  author = {Chen, Jie and Safro, Ilya},
  title = {A Measure of the Local Connectivity between Graph Vertices},
  journal = {Procedia Computer Science},
  year = {2011},
  volume = {4},
  pages = {196--205},
  doi = {10.1016/j.procs.2011.04.021},
}

Scientific Computing

Computing f(A)b via Least Squares Polynomial Approximations

Jie Chen, Mihai Anitescu, and Yousef Saad

SIAM Journal on Scientific Computing (SISC), 33(1):195–222, 2011

PDF Bib DOI Code

@article{fAb,
  author = {Chen, Jie and Anitescu, Mihai and Saad, Yousef},
  title = {Computing {$f(A)b$} via Least Squares Polynomial Approximations},
  journal = {SIAM Journal on Scientific Computing (SISC)},
  year = {2011},
  volume = {33},
  number = {1},
  pages = {195--222},
  doi = {10.1137/090778250},
}

Data Science

Trace Optimization and Eigenproblems in Dimension Reduction Methods

Effrosyni Kokiopoulou, Jie Chen, and Yousef Saad

Numerical Linear Algebra with Applications (NLAA), 18(3):565–602, 2011

PDF Bib DOI

@article{dim.reduct.eig.problems,
  author = {Kokiopoulou, Effrosyni and Chen, Jie and Saad, Yousef},
  title = {Trace Optimization and Eigenproblems in Dimension Reduction Methods},
  journal = {Numerical Linear Algebra with Applications (NLAA)},
  year = {2011},
  volume = {18},
  number = {3},
  pages = {565--602},
  doi = {10.1002/nla.743}
}

Scientific Computing

A Deflated Version of the Block Conjugate Gradient Algorithm with an Application to Gaussian Process Maximum Likelihood Estimation

Jie Chen

Technical Report ANL/MCS-P1927-0811, Argonne National Laboratory, 2011

PDF Bib

@techreport{dbpcg,
  author = {Chen, Jie},
  title = {A Deflated Version of the Block Conjugate Gradient Algorithm with an Application to {G}aussian Process Maximum Likelihood Estimation},
  institution = {Argonne National Laboratory},
  year = {2011},
  number = {ANL/MCS-P1927-0811},
}

2009

Data Science

Divide and Conquer Strategies for Effective Information Retrieval

Jie Chen and Yousef Saad

In Proceedings of the Ninth SIAM International Conference on Data Mining (SDM), 2009

PDF Bib DOI Poster

@inproceedings{divide.and.conquer.lsi,
  author = {Chen, Jie and Saad, Yousef},
  title = {Divide and Conquer Strategies for Effective Information Retrieval},
  booktitle = {Proceedings of the Ninth SIAM International Conference on Data Mining (SDM)},
  year = {2009},
  doi = {10.1137/1.9781611972795.39},
}

Data Science

Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection

Jie Chen, Haw-Ren Fang, and Yousef Saad

Journal of Machine Learning Research (JMLR), 10(Sep):1989–2012, 2009

PDF Bib JOURNAL Code

@article{divide.and.conquer.knn,
  author = {Chen, Jie and Fang, Haw-Ren and Saad, Yousef},
  title = {Fast Approximate {$k$NN} Graph Construction for High Dimensional Data via Recursive {L}anczos Bisection},
  journal = {Journal of Machine Learning Research (JMLR)},
  year = {2009},
  volume = {10},
  number = {Sep},
  pages = {1989--2012},
}

Data Science

Lanczos Vectors versus Singular Vectors for Effective Dimension Reduction

Jie Chen and Yousef Saad

IEEE Transactions on Knowledge and Data Engineering (TKDE), 21(8):1091–1103, 2009

PDF Bib DOI

@article{lanczos.lsi,
  author = {Chen, Jie and Saad, Yousef},
  title = {{L}anczos Vectors versus Singular Vectors for Effective Dimension Reduction},
  journal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},
  year = {2009},
  volume = {21},
  number = {8},
  pages = {1091--1103},
  doi = {10.1109/TKDE.2008.228}
}

Scientific Computing

On the Tensor SVD and the Optimal Low Rank Orthogonal Approximation of Tensors

Jie Chen and Yousef Saad

SIAM Journal on Matrix Analysis and Applications (SIMAX), 30(4):1709–1734, 2009

PDF Bib DOI Code Slides SIAM Student Paper Prize

@article{tensor.svd,
  author = {Chen, Jie and Saad, Yousef},
  title = {On the Tensor {SVD} and the Optimal Low Rank Orthogonal Approximation of Tensors},
  journal = {SIAM Journal on Matrix Analysis and Applications (SIMAX)},
  year = {2009},
  volume = {30},
  number = {4},
  pages = {1709--1734},
  doi = {10.1137/070711621},
}

2008

Graphics and Vision

Architectural Modeling from Sparsely Scanned Range Data

Jie Chen and Baoquan Chen

International Journal of Computer Vision (IJCV), 78(2-3):223–236, 2008

PDF Bib DOI Slides Video Website

@article{ijcv.range.modeling,
  author = {Chen, Jie and Chen, Baoquan},
  title = {Architectural Modeling from Sparsely Scanned Range Data},
  journal = {International Journal of Computer Vision (IJCV)},
  year = {2008},
  volume = {78},
  number = {2-3},
  pages = {223--236},
  doi = {10.1007/s11263-007-0105-5},
}