Jie Chen

According to our database, Jie Chen authored at least 67 papers between 2008 and 2023.

Collaborative distances:

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

2023

Parallel and distributed asynchronous adaptive stochastic gradient methods.

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

Math. Program. Comput., September, 2023

A Decentralized Primal-Dual Framework for Non-Convex Smooth Consensus Optimization.

Gabriel Mancino-Ball, Yangyang Xu, Jie Chen

IEEE Trans. Signal Process., 2023

Bridging mean-field games and normalizing flows with trajectory regularization.

Han Huang, Jiajia Yu, Jie Chen, Rongjie Lai

J. Comput. Phys., 2023

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, Jie Chen

CoRR, 2023

GUAP: Graph Universal Attack Through Adversarial Patching.

Xiao Zang, Jie Chen, Bo Yuan

CoRR, 2023

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

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

Proceedings of the International Conference on Machine Learning, 2023

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

Zehao Niu, Mihai Anitescu, Jie Chen

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Proximal Stochastic Recursive Momentum Methods for Nonconvex Composite Decentralized Optimization.

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

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

2022

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

Yangyang Xu, Yibo Xu, Yonggui Yan, Jie Chen

SIAM J. Imaging Sci., 2022

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, Jie Chen

Proceedings of Machine Learning and Systems 2022, 2022

Data-Efficient Graph Grammar Learning for Molecular Generation.

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

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Distributed stochastic inertial methods with delayed derivatives.

Yangyang Xu, Yibo Xu, Yonggui Yan, Jie Chen

CoRR, 2021

Graph coarsening: From scientific computing to machine learning.

Jie Chen, Yousef Saad, Zechen Zhang

CoRR, 2021

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 R. Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Ulrich Finkler

CoRR, 2021

Noise Injection-based Regularization for Point Cloud Processing.

Xiao Zang, Yi Xie, Siyu Liao, Jie Chen, Bo Yuan

CoRR, 2021

Generating a Doppelganger Graph: Resembling but Distinct.

Yuliang Ji, Ru Huang, Jie Chen, Yuanzhe Xi

CoRR, 2021

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 R. Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, Frederick Reiss

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

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

Xiao Zang, Yi Xie, Jie Chen, Bo Yuan

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Directed Acyclic Graph Neural Networks.

Veronika Thost, Jie Chen

Proceedings of the 9th International Conference on Learning Representations, 2021

Discrete Graph Structure Learning for Forecasting Multiple Time Series.

Chao Shang, Jie Chen, Jinbo Bi

Proceedings of the 9th International Conference on Learning Representations, 2021

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, Georgios Kollias

Proceedings of the 2nd Symposium on Algorithmic Principles of Computer Systems, 2021

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

Tengfei Ma, Jie Chen

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

2020

Asynchronous parallel adaptive stochastic gradient methods.

Yangyang Xu, Colin Sutcher-Shepard, Yibo Xu, Jie Chen

CoRR, 2020

CAG: A Real-Time Low-Cost Enhanced-Robustness High-Transferability Content-Aware Adversarial Attack Generator.

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

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

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, Charles E. Leiserson

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

Online Planner Selection with Graph Neural Networks and Adaptive Scheduling.

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

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

Embedding Compression with Isotropic Iterative Quantization.

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

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

2019

Relaxation-Based Coarsening for Multilevel Hypergraph Partitioning.

Ruslan Shaydulin, Jie Chen, Ilya Safro

Multiscale Model. Simul., 2019

Chart Auto-Encoders for Manifold Structured Data.

Stefan C. Schonsheck, Jie Chen, Rongjie Lai

CoRR, 2019

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, Charles E. Leiserson

CoRR, 2019

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

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

CoRR, 2019

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs.

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

CoRR, 2019

Graph Enhanced Cross-Domain Text-to-SQL Generation.

Siyu Huo, Tengfei Ma, Jie Chen, Maria Chang, Lingfei Wu, Michael Witbrock

Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing, 2019

DAG-GNN: DAG Structure Learning with Graph Neural Networks.

Yue Yu, Jie Chen, Tian Gao, Mo Yu

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

A Sequential Set Generation Method for Predicting Set-Valued Outputs.

Tian Gao, Jie Chen, Vijil Chenthamarakshan, Michael Witbrock

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

2018

Learning low-complexity autoregressive models via proximal alternating minimization.

Fu Lin, Jie Chen

Syst. Control. Lett., 2018

A posteriori error estimate for computing tr(f(A)) by using the Lanczos method.

Jie Chen, Yousef Saad

Numer. Linear Algebra Appl., 2018

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, Tao B. Schardl

CoRR, 2018

Adaptive Planner Scheduling with Graph Neural Networks.

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

CoRR, 2018

Stochastic Gradient Descent with Biased but Consistent Gradient Estimators.

Jie Chen, Ronny Luss

CoRR, 2018

Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders.

Tengfei Ma, Jie Chen, Cao Xiao

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling.

Jie Chen, Tengfei Ma, Cao Xiao

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

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

Shashanka Ubaru, Jie Chen, Yousef Saad

SIAM J. Matrix Anal. Appl., 2017

Hierarchically Compositional Kernels for Scalable Nonparametric Learning.

Jie Chen, Haim Avron, Vikas Sindhwani

J. Mach. Learn. Res., 2017

Learning low-complexity autoregressive models with limited time sequence data.

Fu Lin, Jie Chen

Proceedings of the 2017 American Control Conference, 2017

2016

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

Jie Chen

SIAM J. Sci. Comput., 2016

Analysis and Practical Use of Flexible BiCGStab.

Jie Chen, Lois C. McInnes, Hong Zhang

J. Sci. Comput., 2016

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

Jie Chen, Dehua Cheng, Yan Liu

CoRR, 2016

Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability.

Lingfei Wu, Ian En-Hsu Yen, Jie Chen, Rui Yan

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Efficient one-vs-one kernel ridge regression for speech recognition.

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

Proceedings of the 2016 IEEE International Conference on Acoustics, 2016

2014

A Fast Summation Tree Code for Matérn Kernel.

Jie Chen, Lei Wang, Mihai Anitescu

SIAM J. Sci. Comput., 2014

A parallel linear solver for multilevel Toeplitz systems with possibly several right-hand sides.

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

Parallel Comput., 2014

2013

On the Use of Discrete Laplace Operator for Preconditioning Kernel Matrices.

Jie Chen

SIAM J. Sci. Comput., 2013

Parallelizing the Conjugate Gradient Algorithm for Multilevel Toeplitz Systems.

Jie Chen, Tom L. H. Li

Proceedings of the International Conference on Computational Science, 2013

2012

Dense Subgraph Extraction with Application to Community Detection.

Jie Chen, Yousef Saad

IEEE Trans. Knowl. Data Eng., 2012

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

Mihai Anitescu, Jie Chen, Lei Wang

SIAM J. Sci. Comput., 2012

Difference Filter Preconditioning for Large Covariance Matrices.

Michael L. Stein, Jie Chen, Mihai Anitescu

SIAM J. Matrix Anal. Appl., 2012

2011

Algebraic Distance on Graphs.

Jie Chen, Ilya Safro

SIAM J. Sci. Comput., 2011

Computing f(A)b via Least Squares Polynomial Approximations.

Jie Chen, Mihai Anitescu, Yousef Saad

SIAM J. Sci. Comput., 2011

A measure of the local connectivity between graph vertices.

Jie Chen, Ilya Safro

Proceedings of the International Conference on Computational Science, 2011

Trace optimization and eigenproblems in dimension reduction methods.

Effrosini Kokiopoulou, Jie Chen, Yousef Saad

Numer. Linear Algebra Appl., 2011

2009

Lanczos Vectors versus Singular Vectors for Effective Dimension Reduction.

Jie Chen, Yousef Saad

IEEE Trans. Knowl. Data Eng., 2009

Fast Approximate <i>k</i>NN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection.

Jie Chen, Haw-ren Fang, Yousef Saad

J. Mach. Learn. Res., 2009

A Measure of the Connection Strengths between Graph Vertices with Applications

Jie Chen, Ilya Safro

CoRR, 2009

Divide and Conquer Strategies for Effective Information Retrieval.

Jie Chen, Yousef Saad

Proceedings of the SIAM International Conference on Data Mining, 2009

2008

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

Jie Chen, Yousef Saad

SIAM J. Matrix Anal. Appl., 2008

Architectural Modeling from Sparsely Scanned Range Data.

Jie Chen, Baoquan Chen

Int. J. Comput. Vis., 2008