Artificial Intelligence in General

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, 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, 2016.
[ Manuscript ] [ DOI ]

Statistics and Statistical Machine Learning

Learning Low-Complexity Autoregressive Models with Limited Time Sequence Data
Fu Lin and Jie Chen
Proceedings of the 2017 American Control Conference, 2017.
[ Manuscript ]

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.

Hierarchically Compositional Kernels for Scalable Nonparametric Learning
Jie Chen, Haim Avron and Vikas Sindhwani
Preprint arXiv:1610.08861, 2016.

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.

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 ]

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, 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 ]

Numerical Linear Algebra, Scientific Computing

A Posteriori Error Estimate for Computing $\tr(f(A))$ by Using the Lanczos Method
Jie Chen and Yousef Saad
Technical Report, IBM Thomas J. Watson Research Center, 2017.

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.

Data Structure and Algorithms for Recursively Low-Rank Compressed Matrices
Jie Chen
Preprint ANL/MCS-P5112-0314, Argonne National Laboratory, 2014.

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.
*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 ]

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.

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)

Data Mining, Machine Learning

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, 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 ]

Graphics and Vision

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.