Recent papers may not all appear at the top of this page due to categorization. For chronological ordering, use Google Scholar.
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
Proceedings of Machine Learning and Systems 5 (MLSys), 2023.
[ Manuscript ] [ Proceedings ] [ SALIENT++ Software ]
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
Proceedings of Machine Learning and Systems 4 (MLSys), 2022.
[ Manuscript ] [ Proceedings ] [ SALIENT Software ] [ MIT News ]
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.
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
Findings of the Association for Computational Linguistics: EMNLP 2023 (Findings), 2023.
[ Manuscript ] [ Proceedings ]
Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs
Tengfei Ma, Trong Nghia Hoang, and Jie Chen
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI), 2023.
[ Manuscript ] [ Proceedings ]
A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
Yifan Chen, Rentian Yao, Yun Yang, and Jie Chen
Proceedings of the Fortieth International Conference on Machine Learning (ICML), 2023.
[ Manuscript ] [ Proceedings ]
GC-Flow: A Graph-Based Flow Network for Effective Clustering
Tianchun Wang, Farzaneh Mirzazadeh, Xiang Zhang, and Jie Chen
Proceedings of the Fortieth International Conference on Machine Learning (ICML), 2023.
[ Manuscript ] [ Proceedings ] [ Poster ]
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
Proceedings of the Fortieth International Conference on Machine Learning (ICML), 2023.
[ Manuscript ] [ Proceedings ] [ MIT News ]
GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data
Xinwei Zhang, Mingyi Hong, and Jie Chen
Preprint arXiv:2303.09531, 2023.
Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
Zehao Niu, Mihai Anitescu, and Jie Chen
Proceedings of the Eleventh International Conference on Learning Representations (ICLR), 2023.
[ Manuscript ] [ Conference site ]
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
Enyan Dai and Jie Chen
Proceedings of the Tenth International Conference on Learning Representations (ICLR), 2022.
[ Manuscript ] [ Conference site ] [ Slides ] [ MIT News ]
Data-Efficient Graph Grammar Learning for Molecular Generation
Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, and Wojciech Matusik
Proceedings of the Tenth International Conference on Learning Representations (ICLR), 2022.
[ Manuscript ] [ Conference site ] [ YouTube Video ] [ MIT News ]
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
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks), 2021.
[ Manuscript ] [ Proceedings ]
Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models
Xiao Zang, Yi Xie, Jie Chen, and Bo Yuan
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI), 2021.
[ Manuscript ] [ DOI ]
Generating a Doppelganger Graph: Resembling but Distinct
Yuliang Ji, Ru Huang, Jie Chen, and Yuanzhe Xi
Preprint arXiv:2101.09593, 2021.
Discrete Graph Structure Learning for Forecasting Multiple Time Series
Chao Shang, Jie Chen, and Jinbo Bi
Proceedings of the Ninth International Conference on Learning Representations (ICLR), 2021.
[ Manuscript ] [ Conference site ] [ Poster ]
Directed Acyclic Graph Neural Networks
Veronika Thost and Jie Chen
Proceedings of the Ninth International Conference on Learning Representations (ICLR), 2021.
[ Manuscript ] [ Conference site ]
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
Tengfei Ma and Jie Chen
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021.
[ Manuscript ] [ DOI ] [ Slides ]
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
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.
[ Manuscript ] [ DOI ] [ Poster ]
Embedding Compression with Isotropic Iterative Quantization
Siyu Liao, Jie Chen, Yanzhi Wang, Qinru Qiu, and Bo Yuan
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.
[ Manuscript ] [ DOI ]
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
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.
[ Manuscript ] [ DOI ]
Online Planner Selection with Graph Neural Networks and Adaptive Scheduling
Tengfei Ma, Patrick Ferber, Siyu Huo, Jie Chen, and Michael Katz
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.
[ Manuscript ] [ DOI ] [ Slides ]
*The data set is presented at ICML 2019 workshop:
IPC: A Benchmark Data Set for Learning with Graph-Structured Data
Patrick Ferber, Tengfei Ma, Siyu Huo, Jie Chen, and Michael Katz
ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data, 2019.
[ Manuscript ] [ Workshop site ] [ Poster ] [ Data set ]
Chart Auto-Encoders for Manifold Structured Data
Stefan Schonsheck, Jie Chen, and Rongjie Lai
Preprint arXiv:1912.10094, 2019.
DAG-GNN: DAG Structure Learning with Graph Neural Networks
Yue Yu, Jie Chen, Tian Gao, and Mo Yu
Proceedings of the Thirty-sixth International Conference on Machine Learning (ICML), 2019.
[ Manuscript ] [ Proceedings ] [ Slides ]
A Sequential Set Generation Method for Predicting Set-Valued Outputs
Tian Gao, Jie Chen, Vijil Chenthamarakshan, and Michael Witbrock
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019.
[ Manuscript ] [ DOI ]
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
Tengfei Ma, Jie Chen, and Cao Xiao
Advances in Neural Information Processing Systems 31 (NeurIPS), 2018.
[ Manuscript ] [ Proceedings ] [ Poster ]
Stochastic Gradient Descent with Biased but Consistent Gradient Estimators
Jie Chen and Ronny Luss
Preprint arXiv:1807.11880, 2018.
[ Software ]
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
Jie Chen, Tengfei Ma, and Cao Xiao
Proceedings of the Sixth International Conference on Learning Representations (ICLR), 2018.
[ Manuscript ] [ Conference site ] [ Poster ] [ PyTorch Code ]
Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability
Lingfei Wu, Ian E.H. Yen, Jie Chen, and Rui Yan
Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016.
[ Manuscript ] [ DOI ]
Efficient One-Vs-One Kernel Ridge Regression for Speech Recognition
Jie Chen, Lingfei Wu, Kartik Audhkhasi, Brian Kingsbury, and Bhuvana Ramabhadran
Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016.
[ Manuscript ] [ DOI ]
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, 177:107406, 2024.
[ Manuscript ] [ DOI ]
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
NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning, 2021.
[ Manuscript ] [ Workshop site ]
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
2nd KDD Workshop on Anomaly Detection in Finance, 2019.
[ Manuscript ] [ Workshop site ] [ Data set ]
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
NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy, 2018.
[ Manuscript ] [ Workshop site ]
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
Proceedings of the Fortieth International Conference on Machine Learning (ICML), 2023.
[ Manuscript ] [ Proceedings ]
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, 15:471--508, 2023.
[ Manuscript ] [ DOI ] [ Software ]
A Decentralized Primal-Dual Framework for Non-convex Smooth Consensus Optimization
Gabriel Mancino-Ball, Yangyang Xu, and Jie Chen
IEEE Transactions on Signal Processing, 71:525--538, 2023.
[ Manuscript ] [ DOI ]
Proximal Stochastic Recursive Momentum Methods for Nonconvex Composite Decentralized Optimization
Gabriel Mancino-Ball, Shengnan Miao, Yangyang Xu, and Jie Chen
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 2023.
[ Manuscript ] [ DOI ]
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, 15(2):550--590, 2022.
[ Manuscript ] [ DOI ]
Learning Low-Complexity Autoregressive Models via Proximal Alternating Minimization
Fu Lin and Jie Chen
Systems & Control Letters, 122:48--53, 2018.
[ Manuscript ] [ DOI ]
*a short version appears in ACC 2017:
Learning Low-Complexity Autoregressive Models with Limited Time Sequence Data
Fu Lin and Jie Chen
Proceedings of the 2017 American Control Conference (ACC), 2017.
[ Manuscript ] [ DOI ]
Scalable Computation of Regularized Precision Matrices via Stochastic Optimization
Yves F. Atchade, Rahul Mazumder, and Jie Chen
Technical Report RC25543, IBM Thomas J. Watson Research Center, 2015.
[ Manuscript ]
Linear-Cost Covariance Functions for Gaussian Random Fields
Jie Chen and Michael L. Stein
Journal of the American Statistical Association, 118(541):147--164, 2023.
[ Manuscript ] [ DOI ] [ Software ] [ Slides ]
Hierarchically Compositional Kernels for Scalable Nonparametric Learning
Jie Chen, Haim Avron, and Vikas Sindhwani
Journal of Machine Learning Research, 18(66):1--42, 2017.
[ Manuscript ] [ Journal site ]
An Inversion-Free Estimating Equations Approach for Gaussian Process Models
Mihai Anitescu, Jie Chen, and Michael L. Stein
Journal of Computational and Graphical Statistics, 26(1):98--107, 2017.
[ Manuscript ] [ DOI ]
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.
Stochastic Approximation of Score Functions for Gaussian Processes
Michael L. Stein, Jie Chen, and Mihai Anitescu
Annals of Applied Statistics, 7(2):1162--1191, 2013.
[ Manuscript ] [ DOI ] [ Software ]
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
Symposium on Algorithmic Principles of Computer Systems, 2021.
[ Manuscript ] [ DOI ]
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, 40(8):408--424, 2014.
[ Manuscript ] [ DOI ]
*a partial version appears in ICCS 2013:
Parallelizing the Conjugate Gradient Algorithm for Multilevel Toeplitz Systems
Jie Chen and Tom L. H. Li
Procedia Computer Science, 18:571--580, 2013.
[ Manuscript ] [ DOI ] [ Slides ]
A Parallel Tree Code for Computing Matrix-Vector Products with the Matern Kernel
Jie Chen, Lei Wang, and Mihai Anitescu
Preprint ANL/MCS-P5015-0913, Argonne National Laboratory, 2013.
[ Manuscript ] [ Software ] [ Gallery ]
Bridging Mean-Field Games and Normalizing Flows with Trajectory Regularization
Han Huang, Jiajia Yu, Jie Chen, and Rongjie Lai
Journal of Computational Physics, 487:112155, 2023.
[ Manuscript ] [ DOI ]
Graph Neural Networks for Selection of Preconditioners and Krylov Solvers
Ziyuan Tang, Hong Zhang, and Jie Chen
NeurIPS 2022 New Frontiers in Graph Learning Workshop, 2022.
[ Manuscript ] [ Workshop site ]
Relaxation-Based Coarsening for Multilevel Hypergraph Partitioning
Ruslan Shaydulin, Jie Chen, and Ilya Safro
Multiscale Modeling & Simulation, 17(1):482--506, 2019.
[ Manuscript ] [ DOI ]
A Posteriori Error Estimate for Computing $tr(f(A))$ by Using the Lanczos Method
Jie Chen and Yousef Saad
Numerical Linear Algebra with Applications, 25(5):e2170, 2018.
[ Manuscript ] [ DOI ]
Fast Estimation of $tr(f(A))$ via Stochastic Lanczos Quadrature
Shashanka Ubaru, Jie Chen, and Yousef Saad
SIAM Journal on Matrix Analysis and Applications, 38(4):1075--1099, 2017.
[ Manuscript ] [ DOI ]
How Accurately Should I Compute Implicit Matrix-Vector Products When Applying the Hutchinson Trace Estimator?
Jie Chen
SIAM Journal on Scientific Computing, 38(6):A3515--A3539, 2016.
[ Manuscript ] [ DOI ]
Analysis and Practical Use of Flexible BiCGStab
Jie Chen, Lois Curfman McInnes, and Hong Zhang
Journal of Scientific Computing, 68(2):803--825, 2016.
[ Manuscript ] [ DOI ]
Computing Square Root Factorization for Recursively Low-Rank Compressed Matrices
Jie Chen
Technical Report RC25499, IBM Thomas J. Watson Research Center, 2014.
[ Manuscript ]
Data Structure and Algorithms for Recursively Low-Rank Compressed Matrices
Jie Chen
Preprint ANL/MCS-P5112-0314, Argonne National Laboratory, 2014.
[ Manuscript ]
A Stable Scaling of Newton-Schulz for Improving the Sign Function Computation of a Hermitian Matrix
Jie Chen and Edmond Chow
Preprint ANL/MCS-P5059-0114, Argonne National Laboratory, 2014.
[ Manuscript ]
*The method in a previously circulated version "A Newton-Schulz Variant for Improving the Initial Convergence in Matrix Sign Computation" is unstable. Readers should not follow that method.
A Fast Summation Tree Code for Matern Kernel
Jie Chen, Lei Wang, and Mihai Anitescu
SIAM Journal on Scientific Computing, 36(1):A289--A309, 2014.
[ Manuscript ] [ DOI ]
On the Use of Discrete Laplace Operator for Preconditioning Kernel Matrices
Jie Chen
SIAM Journal on Scientific Computing, 35(2):A577--A602, 2013.
[ Manuscript ] [ DOI ] [ Slides ]
Difference Filter Preconditioning for Large Covariance Matrices
Michael L. Stein, Jie Chen, and Mihai Anitescu
SIAM Journal on Matrix Analysis and Applications, 33(1):52--72, 2012.
[ Manuscript ] [ DOI ]
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, 34(1):A240--A262, 2012.
[ Manuscript ] [ DOI ] [ Software ] [ Slides ] [ ScalaGAUSS website ]
A Deflated Version of the Block Conjugate Gradient Algorithm with an Application to Gaussian Process Maximum Likelihood Estimation
Jie Chen
Preprint ANL/MCS-P1927-0811, Argonne National Laboratory, 2011.
[ Manuscript ]
Algebraic Distance on Graphs
Jie Chen and Ilya Safro
SIAM Journal on Scientific Computing, 33(6):3468--3490, 2011.
[ Manuscript ] [ DOI ]
*a short version appears in ICCS 2011:
A Measure of the Local Connectivity between Graph Vertices
Jie Chen and Ilya Safro
Procedia Computer Science, 4:196--205, 2011.
[ Manuscript ] [ DOI ]
Computing $f(A)b$ via Least Squares Polynomial Approximations
Jie Chen, Mihai Anitescu, and Yousef Saad
SIAM Journal on Scientific Computing, 33(1):195--222, 2011.
[ Manuscript ] [ DOI ] [ Software ]
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, 30(4):1709--1734, 2009.
[ Manuscript ] [ DOI ] [ Software ] [ Slides ] (SIAM Student Paper Prize)
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, 79(1):187--223, 2022.
[ Manuscript ] [ DOI ]
Dense Subgraph Extraction with Application to Community Detection
Jie Chen and Yousef Saad
IEEE Transactions on Knowledge and Data Engineering, 24(7):1216--1230, 2012.
[ Manuscript ] [ DOI ]
Trace Optimization and Eigenproblems in Dimension Reduction Methods
Effrosyni Kokiopoulou, Jie Chen, and Yousef Saad
Numerical Linear Algebra with Applications, 18(3):565--602, 2011.
[ Manuscript ] [ DOI ]
Divide and Conquer Strategies for Effective Information Retrieval
Jie Chen and Yousef Saad
Proceedings of the 2009 SIAM International Conference on Data Mining (SDM), 2009.
[ Manuscript ] [ DOI ] [ Poster ]
Fast Approximate $k$NN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection
Jie Chen, Haw-ren Fang, and Yousef Saad
Journal of Machine Learning Research, 10(Sep):1989--2012, 2009.
[ Manuscript ] [ Journal site ] [ Software ]
Lanczos Vectors versus Singular Vectors for Effective Dimension Reduction
Jie Chen and Yousef Saad
IEEE Transactions on Knowledge and Data Engineering, 21(8):1091--1103, 2009.
[ Manuscript ] [ DOI ]
Architectural Modeling from Sparsely Scanned Range Data
Jie Chen and Baoquan Chen
International Journal of Computer Vision, 78(2-3):223--236, 2008.
[ Manuscript* ] [ DOI ] [ Slides ] [ Video (15.3MB) ] [ Scanning project website ]
*The original publication is available at www.springerlink.com.