Jie Chen
Jie Chen
MIT-IBM Watson AI Lab, IBM Research
Verified email at - Homepage
Cited by
Cited by
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
J Chen, T Ma, C Xiao
International Conference on Learning Representations (ICLR), 2018
EvolveGCN: Evolving graph convolutional networks for dynamic graphs
A Pareja, G Domeniconi, J Chen, T Ma, T Suzumura, H Kanezashi, ...
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5363-5370, 2020
Fast approximate kNN graph construction for high dimensional data via recursive Lanczos bisection
J Chen, H Fang, Y Saad
Journal of Machine Learning Research 10 (Sep), 1989-2012, 2009
Dense subgraph extraction with application to community detection
J Chen, Y Saad
IEEE Transactions on Knowledge and Data Engineering 24 (7), 1216-1230, 2012
Trace optimization and eigenproblems in dimension reduction methods
E Kokiopoulou, J Chen, Y Saad
Numerical Linear Algebra with Applications 18 (3), 565-602, 2011
Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics
M Weber, G Domeniconi, J Chen, DKI Weidele, C Bellei, T Robinson, ...
arXiv preprint arXiv:1908.02591, 2019
DAG-GNN: DAG structure learning with graph neural networks
Y Yu, J Chen, T Gao, M Yu
International Conference on Machine Learning, 7154-7163, 2019
Constrained generation of semantically valid graphs via regularizing variational autoencoders
T Ma, J Chen, C Xiao
Advances in Neural Information Processing Systems 31, 7113-7124, 2018
Fast Estimation of via Stochastic Lanczos Quadrature
S Ubaru, J Chen, Y Saad
SIAM Journal on Matrix Analysis and Applications 38 (4), 1075-1099, 2017
Architectural modeling from sparsely scanned range data
J Chen, B Chen
International Journal of Computer Vision 78 (2), 223-236, 2008
On the tensor SVD and the optimal low rank orthogonal approximation of tensors
J Chen, Y Saad
SIAM Journal on Matrix Analysis and Applications 30 (4), 1709-1734, 2009
Stochastic approximation of score functions for Gaussian processes
M Stein, J Chen, M Anitescu
Annals of Applied Statistics 7 (2), 1162--1191, 2013
A matrix-free approach for solving the parametric Gaussian process maximum likelihood problem
M Anitescu, J Chen, L Wang
SIAM Journal on Scientific Computing 34 (1), A240-A262, 2012
Algebraic distance on graphs
J Chen, I Safro
SIAM Journal on Scientific Computing 33 (6), 3468-3490, 2011
Computing f(A)b via Least Squares Polynomial Approximations
J Chen, M Anitescu, Y Saad
SIAM Journal on Scientific Computing 33 (1), 195-222, 2011
Lanczos vectors versus singular vectors for effective dimension reduction
J Chen, Y Saad
IEEE Transactions on Knowledge and Data Engineering 21 (8), 1091-1103, 2009
Scalable Graph Learning for Anti-Money Laundering: A First Look
M Weber, J Chen, T Suzumura, A Pareja, T Ma, H Kanezashi, T Kaler, ...
arXiv preprint arXiv:1812.00076, 2018
Project CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks
R Puri, DS Kung, G Janssen, W Zhang, G Domeniconi, V Zolotov, J Dolby, ...
arXiv preprint arXiv:2105.12655, 2021
Stochastic Gradient Descent with Biased but Consistent Gradient Estimators
J Chen, R Luss
arXiv preprint arXiv:1807.11880, 2018
Revisiting random binning features: Fast convergence and strong parallelizability
L Wu, IEH Yen, J Chen, R Yan
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining …, 2016
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