Publications
Technical reports
Lucassen M., Suykens J.A.K., Batselier K., Tensor Network Kalman filtering for large-scale LS-SVMs.
Batselier K., The trouble with tensor ring decompositions.
Batselier K., Wong N., A QR Algorithm for Symmetric Tensors.
Batselier K., Dreesen P., De Moor B., SVD-based removal of the multiplicities of all roots of a multivariate polynomial system.
Book Chapters
Chen C., Batselier K., Wong N., Tensor Network Algorithms for Image Classification. In Yipeng Liu. Tensors for Data Processing, Chapter 9, Elsevier, 2022, Pages 249-291.
Journal publications
Li L., Yu W., Batselier K., Faster Tensor Train Decomposition for Sparse Data, Journal of Computational and Applied Mathematics, vol. 405, May. 2022.
Menzen C., Kok M., Batselier K., Alternating linear scheme in a Bayesian framework for low-rank tensor approximation, SIAM Journal on Scientific Computing, 44(3):A1116-44, 2022.
Chen C., Batselier K., Yu W., Wong N., Kernelized Support Tensor Train Machines, Pattern Recognition, vol. 122, 2022.
Batselier K., Low-rank tensor decompositions for nonlinear system identification, in IEEE Control Systems Magazine, vol. 42, no. 1, pp. 54-74, Feb. 2022.
Batselier K., Cichocki A., Wong N., MERACLE: Constructive layer-wise conversion of a Tensor Train into a MERA, Commun. Appl. Math. Comput. 3, 257–279, 2021.
Karagoz R., Batselier K., Nonlinear system identification with regularized Tensor Network B-splines, Automatica, vol. 122, 2020.
Ko. C.-Y., Batselier K., Daniel L., Yu W., Wong N., Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains, IEEE Transactions on Image Processing, vol. 29, pp. 6918-6931, 2020.
Ko. C.-Y., Chen C., He Z., Zhang Y., Batselier K., Wong N., Deep Model Compression and Inference Speedup of Sum-Product Networks on Tensor Trains, IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 7, pp. 2665-2671, July 2020.
Batselier K., Yu W., Daniel L., Wong N., Computing low-rank approximations of large-scale matrices with the Tensor Network randomized SVD, SIAM Journal on Matrix Analysis and Applications, vol. 39, no.3, 2018, pp. 1221–1244.
Batselier K., Wong N., Matrix output extension of the tensor network Kalman filter with an application in MIMO Volterra system identification, Automatica, vol. 95, 2018, pp. 413-418.
Batselier K., Ko C.-Y., Wong N., Tensor network subspace identification of polynomial state space models, Automatica, vol. 95, 2018, pp. 187-196.
Chen Z., Batselier K., Suykens J.A.K., Wong N., Parallelized Tensor Train Learning of Polynomial Classifiers, IEEE Transactions on Neural Networks and Learning Systems, vol. 29, 10, 2018, pp. 4621-4632 .
Dreesen P., Batselier K., De Moor B., Multidimensional Realization Theory and Polynomial System Solving, International Journal of Control, vol 91, pp. 2692-2704.
Batselier K., Chen Z., Wong N., A Tensor Network Kalman filter with an application in recursive MIMO Volterra system identification, Automatica, vol. 84, 2017, pp. 17-25.
Batselier K., Chen Z., Wong N., Tensor Network alternating linear scheme for MIMO Volterra system identification, Automatica, vol. 84, 2017, pp. 26-35.
Batselier K., Wong N., A constructive arbitrary-degree Kronecker product decomposition of tensors, Numerical Linear Algebra with Applications, vol. 24, 5, 2017.
Batselier K., Wong N., Inverse multivariate polynomial root-finding: Numerical implementations of the affine and projective Buchberger–Möller algorithm, Journal of Computational and Applied Mathematics, Volume 320, Aug. 2017, pp. 15-29.
Zhang Z., Batselier K., Liu H., Daniel L., Wong N., Tensor Computation: A New Framework for High-Dimensional Problems in EDA, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 36, no. 4, April 2017,pp. 521-536.
Batselier K., Wong N., Symmetric Tensor Decomposition by an Iterative Eigendecomposition Algorithm, Journal of Computational and Applied Mathematics, vol. 308, Dec. 2016, pp. 69-82.
Batselier K., Wong N., Computing the state recursion polynomials for discrete linear mD systems, Automatica, vol. 64, 2016, pp.254-261.
Batselier K., Liu H., Wong N., A Constructive Algorithm for Decomposing a Tensor into a Finite Sum of Orthonormal Rank-1 Terms, SIAM Journal on Matrix Analysis and Applications, vol. 36, no. 3, Sept. 2015, pp. 1315–1337.
Batselier K., Dreesen P., De Moor B., The Canonical Decomposition of \mathcal{C}^n_d and Numerical Gröbner and Border Bases, SIAM Journal on Matrix Analysis and Applications, vol. 35, no. 4, Oct. 2014, pp. 1242-1264.
Batselier K., Dreesen P., De Moor B., On the null spaces of the Macaulay matrix, Linear Algebra and its Applications, vol. 460, no. 1, November 2014, pp. 259-289.
Batselier K., Dreesen P., De Moor B., A fast recursive orthogonalization scheme for the Macaulay matrix, Journal of Computational and Applied Mathematics, vol. 267, Sept. 2014, pp. 20-32.
S. Zhang, H. Liu, K. Batselier and N. Wong, Limit Cycle Identification in Nonlinear Polynomial Systems, Applied Mathematics, Vol. 4 No. 9A, 2013, pp. 19-26.
Batselier K., Dreesen P., De Moor B., A geometrical approach to finding multivariate approximate LCMs and GCDs, Linear Algebra and its Applications, vol. 438, no. 9, May 2013, pp. 3618-3628.
Batselier K., Dreesen P., De Moor B., The Geometry of Multivariate Polynomial Division and Elimination, SIAM Journal on Matrix Analysis and Applications, vol. 34, no. 1, Feb. 2013, pp. 102-125.
Conference publications
Wesel F, Batselier K. Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models. In International Conference on Artificial Intelligence and Statistics 2024 Apr 18 (pp. 1261-1269). PMLR.
De Rooij SJ, Batselier K, Hunyadi B. Enabling large-scale probabilistic seizure detection with a tensor-network Kalman filter for LS-SVM. In 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) 2023 Jun 4 (pp. 1-5). IEEE.
Menzen C, Memmel E, Batselier K, Kok M. Projecting basis functions with tensor networks for Gaussian process regression. IFAC-PapersOnLine. 2023 Jan 1;56(2):7288-93.
Memmel E, Menzen C, Batselier K. Bayesian Framework for a MIMO Volterra Tensor Network. IFAC-PapersOnLine. 2023 Jan 1;56(2):7294-9.
Batselier K. A Khatri-Rao product tensor network for efficient symmetric MIMO Volterra identification. IFAC-PapersOnLine. 2023 Jan 1;56(2):7282-7.
Schuurmans J., Batselier K., Kooij J., How informative is the Approximation Error from Tensor~Decomposition for Neural Network Compression?, in Proc. of The Eleventh International Conference on Learning Representations (ICLR), May 2023.
Wesel F., Batselier K., Tensor-based Kernel Machines with Structured Inducing Points for Large and High-Dimensional Data, in Proc. of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS), April 2023.
Wesel F., Batselier K., Large-Scale Learning with Fourier Features and Tensor Decompositions, in Proc. of the 2021 Conference on Neural Information Processing Systems (NeuRIPS), December 2021.
Batselier K., Enforcing symmetry in tensor network MIMO Volterra identification, in the Proc. of the 19th IFAC Symposium on System Identification (Sysid 2021), Jul. 2021.
Batselier K., Ko C.-Y., Wong N., Extended Kalman Filtering with Low-Rank Tensor Networks for MIMO Volterra System Identification, in Proc. of the 58th IEEE Conference on Decision and Control, Nice, France, Dec. 2019.
Gedon D., Piscaer P., Batselier K., Smith C., Verhaegen M., Tensor Network Kalman Filter for LTI systems, in Proc. 27th European Signal Processing Conference, EUSIPCO, 2019.
Chen C., Batselier K., Ko C.-Y., Wong N., Matrix Product Operator Restricted Boltzmann Machines, in Proc. International Joint Conference on Neural Networks (IJCNN), 2019.
Chen C., Batselier K., Ko C.-Y., Wong N., A Support Tensor Train Machine, in Proc. International Joint Conference on Neural Networks (IJCNN), 2019.
Y. Zhang, C-Y. Ko, C. Chen, K. Batselier and N. Wong, Sparse Tensor Network System Identification for Nonlinear Circuit Macromodeling, in Proc. Intl. Conf. Solid-State and Integrated Circuit Technology (ICSICT), Oct 2018. (Invited Paper)
Batselier K., Ko C.-Y., Phan A.H., Cichocki A., Wong N., Multilinear state space system identification with matrix product operators, in Proc. of the 18th IFAC Symposium on System Identification (Sysid 2018), Stockholm, Sweden, Jul. 2018.
C. Chen, K. Batselier, M. Telescu, S. Azou, N. Tanguy and N. Wong, Tensor-network-based predistorter design for multiple-input multiple-output nonlinear systems, in Proc. IEEE Intl. Conf. on ASIC (ASICON), Oct 2017. (Invited Paper)
C. Chen, K. Batselier and N. Wong, A novel tensor-based model compression method via Tucker and tensor train decompositions, in Proc. Electrical Performance of Electronic Packages and Systems (EPEPS), Oct 2017.
Z. Chen, K. Batselier, H. Lui and N. Wong, An efficient homotopy-based Poincare-Lindstedt method for the periodic steady-state analysis of nonlinear autonomous oscillators, in Proc. Asia and South Pacific Design Automation Conference (ASPDAC), Jan 2017.
K. Batselier, Z. Chen, H. Lui and N. Wong, A tensor-based Volterra series black-box nonlinear system identification and simulation framework, in Prof. Intl. Conf. on Computer-Aided Design (ICCAD), Nov 2016.
J. Deng, H. Liu, K. Batselier, Y. K. Kwok and N. Wong, STORM: a nonlinear model order reduction method via symmetric tensor decomposition, in Proc. Asia and South Pacific Design Automation Conference (ASPDAC), Jan 2016.
H. Liu, X. Xiong, K. Batselier, L. Jiang, L. Daniel and N. Wong, STAVES: speedy tensor-aided Volterra-based electronic simulator , in Prof. Intl. Conf. on Computer-Aided Design (ICCAD), Nov 2015.
K. Batselier, Q. Chen and N. Wong, An adaptive dynamical low-rank tensor approximation scheme for fast circuit simulation, in Proc. Intl. Conf. on ASIC (ASICON), Nov 2015.
Liu H., Batselier K., Wong N., A Novel Linear Algebra Method for the Determination of Periodic Steady States of Nonlinear Oscillators, in Proc. 2014 International Conference on Computer-Aided Design, 2014, San Jose, USA.
Deng J., Batselier K., Zhang Y., Wong N., An Efficient Two-level DC Operating Points Finder for Transistor Circuits, in Proceedings of the 51st Annual Design Automation Conference 2014, San Francisco, USA.
De Meester, J., Batselier, K., Koolen, N., Hunyadi, B., Decuyper, J., Vanden Bosch, E., Vandewalle, J., Dehaene, W, The mathematics in your ears. The role of math in integrated STEM via the modeling of hearing aids, in Proc. of the 41th SEFI Conference. SEFI. Leuven, Belgium, Sep. 2013 (pp. 1-8)
Geebelen D., Batselier K., Dreesen P., Signoretto M., Suykens J.A.K., De Moor B., Vandewalle J., Joint Regression and Linear Combination of Time Series for Optimal Prediction, in Proc. of the European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, Apr. 2012.
Dreesen P., Batselier K., De Moor B., Weighted/Structured Total Least Squares Problems and Polynomial System Solving, in Proc. of the 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Brugge, Belgium, Apr. 2012, pp. 351-356.
Batselier K., Dreesen P., De Moor B., Prediction Error Method Identification is an Eigenvalue Problem, in Proc. of the 16th IFAC Symposium on System Identification (Sysid 2012), Brussels, Belgium, Jul. 2012.
Dreesen P., Batselier K., De Moor B., Back to the Roots: Polynomial System Solving, Linear Algebra, Systems Theory, in Proc. of the 16th IFAC Symposium on System Identification (SYSID 2012), Brussels, Belgium, Jul. 2012, pp. 1203-1208.
Batselier K., Dreesen P., De Moor B., Maximum Likelihood Estimation and Polynomial System Solving, in Proc. of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2012 (ESANN 2012), Brugge, Belgium, Apr. 2012.