All Papers

commutative algebra, linear algebra and algebraic geometry

1. Linearization of resolutions via products. Journal of Pure and Applied Algebra, 2020. pdf

2. Resolution of ideals associated to subspace arrangements (with Aldo Conca). arXiv:1910.01955v2[math.AC], 2020. pdf

3. Results on the algebraic matroid of the determinantal variety. arXiv:2002.05082v5[math.AG], 2020. pdf

4. Homomorphic sensing of subspace arrangements (with Liangzu Peng). arXiv:2006.05158v2[cs.LG], 2020. pdf

5. Determinantal conditions for homomorphic sensing. arXiv:1812.07966v5[math.CO], 2020. pdf

algebraic geometry in machine learning and signal processing

1. M.C. Tsakiris. Low-rank matrix completion theory via Plucker coordinates. arXiv:2004.12430[cs.LG], 2021. pdf

2. Y. Yao, L. Peng, M.C. Tsakiris. Unlabeled principal component analysis. arXiv:2101.09446[cs.LG], 2021. pdf

3. L. Peng, B. Wang, M.C. Tsakiris. Homomorphic sensing: sparsity and noise. International Conference on Machine Learning (ICML), 2021.

4. Y. Yao, L. Peng, M.C. Tsakiris. Unsigned Matrix Completion. IEEE International Symposium on Information Theory, 2021.

5. M.C. Tsakiris, L. Peng, A. Conca, L. Kneip, Y. Shi, H. Choi. An algebraic-geometric approach to linear regression without correspondences. IEEE Transactions on Information Theory, 2020. pdf code

6. L. Peng, X. Song, M.C. Tsakiris, H. Choi, L. Kneip, Y. Shi, Algebraically initialized expectation-maximization for header-free communications, International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.

7. M.C. Tsakiris, L. Peng. Homomorphic sensing. International Conference on Machine Learning (ICML), 2019. pdf

8. W. Xu, L. Hu, M.C. Tsakiris, L. Kneip, Online stability improvement of Groebner basis solvers using deep learning, International Conference on 3D Vision (3DV), 2019.

9. M.C. Tsakiris, R. Vidal. Filtrated algebraic subspace clustering. SIAM Journal of Imaging Sciences, 2017. pdf code

10. M.C. Tsakiris, R. Vidal. Algebraic clustering of affine subspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2017. pdf

11. M.C. Tsakiris, R. Vidal, Filtrated spectral algebraic subspace clustering, International Conference on Computer Vision Workshops (ICCVW), 2015.

12. M.C. Tsakiris, R. Vidal, Abstract algebraic-geometric subspace clustering, Asilomar Conference on Signals, Systems and Computers, 2014.

machine learning, signal processing and control

1. T. Ding, Z. Zhu, M.C. Tsakiris, R. Vidal, D. P. Robinson. Dual principal component pursuit for learning a union of hyperplanes: theory and algorithms. Artificial Intelligence and Statistics (AISTATS), 2021.

2. L. Peng, M.C. Tsakiris. Linear regression without correspondences via concave minimization. IEEE Signal Processing Letters, 2020.

3. M.C. Tsakiris, R. Vidal. Theoretical analysis of sparse subspace clustering with missing entries. International Conference on Machine Learning (ICML), 2018. pdf code

4. T. Ding, Y. Yang, Z. Zhu, D. Robinson, R. Vidal, L. Kneip, M.C. Tsakiris. Robust homography estimation via dual principal component pursuit. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

5. C. Lane, R. Boger, C. You, M.C. Tsakiris, B. D. Haeffele, R. Vidal, Classifying and comparing approaches to subspace clustering with missing data, International Conference on Computer Vision Workshops (ICCVW), 2019.

6. Q. Qu, Z. Zhu, X. Li, M.C. Tsakiris, J. Wright, R. Vidal. Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications, arXiv:2001.06970 [cs.LG], 2019.

7. Z. Zhu, T. Ding, M.C. Tsakiris, D. P. Robinson, R. Vidal, A linearly convergent method for non-convex non-smooth optimization on the Grassmannian with applications to robust subspace and dictionary learning, Neural Information Processing Systems (NeurIPS), 2019.

8. T. Ding, Z. Zhu, T. Ding, Y. Yang, R. Vidal, M.C. Tsakiris, D. P. Robinson, Noisy dual principal component pursuit, International Conference on Machine Learning (ICML), 2019.

9. Z. Zhu, Y. Wang, D. P. Robinson, D. Naiman, R. Vidal, M.C. Tsakiris. Dual principal component pursuit: Improved analysis and efficient algorithms. Neural Information Processing Systems (NeurIPS), 2018. pdf

10. M. C. Tsakiris, R. Vidal. Dual principal component pursuit. Journal of Machine Learning Research (JMLR), 2018. pdf code

11. M.C. Tsakiris, R. Vidal. Hyperplane clustering via dual principal component pursuit. International Conference on Machine Learning (ICML), 2017. pdf

12. M.C. Tsakiris, R. Vidal, Dual principal component pursuit, International Conference on Computer Vision Workshops (ICCVW), 2015.

13. M.C. Tsakiris, D. C. Tarraf, Algebraic decompositions of dynamic programming problems with linear dynamics, Systems and Control Letters, 2015.

14. M.C. Tsakiris, D. C. Tarraf, On subspace decompositions of finite horizon dynamic programming problems with switched linear dynamics, IEEE Conference on Decision and Control (CDC), 2013.

15. M.C. Tsakiris, D. C. Tarraf, On subspace decompositions of finite horizon dynamic programming problems with linear dynamics, IEEE Conference on Decision and Control (CDC), 2012.

16. M.C. Tsakiris, D. C. Tarraf, On decompositions of dynamic programming problems with linear dynamics, Allerton Conference on Communication, Control and Computing, 2012.

17. M. C. Tsakiris, C. G. Lopes, P. A. Naylor. An alternative criterion for regularization in recursive least-squares problems. IEEE International Symposium on Wireless Communication Systems, 2010. pdf

18. M.C. Tsakiris, C. G. Lopes, V. H. Nascimento, An array recursive least-squares algorithm with generic non-fading regularization matrix, IEEE Signal Processing Letters, 2010.

19. M.C. Tsakiris, P. A. Naylor, Fast exact affine projection algorithm using displacement structure theory, International Conference in Signal Processing, 2009.

20. M.C. Tsakiris, C. G. Lopes, A robust affine projection algorithm with feedback compensation of the condition number, International Symposium on Circuits and Systems (ISCAS), 2009.