Minh Ha Quang

Researcher

About

Education

Previous Academic Affiliation

 

Projects

 

Selected Publications

Book

  • Hà Quang Minh and Vittorio Murino (editors). Algorithmic Advances in Riemannian Geometry and Applications: For Machine Learning, Computer Vision, Statistics, and Optimization. Springer series in Advances in Computer Vision and Pattern Recognition, 2016, available online at http://link.springer.com/book/10.1007%2F978-3-319-45026-1 .

Journal Articles

  • Hà Quang Minh, Loris Bazzani, and Vittorio Murino.  A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi- view LearningJournal of Machine Learning Research, 17(25):1−72, 2016, available online at http://www.jmlr.org/papers/v17/14-036.html  (72 pages)
  • Hà Quang Minh and Laurenz Wiskott. Multivariate slow feature analysis and decorrelation filtering for blind source separationIEEE Transactions on Image Processing,  volume 22, issue 7, pages 2737-2750, July 2013.
  • Gianluigi Pillonetto, Minh Ha Quang and Alessandro Chiuso. A New Kernel-based Approach for Nonlinear System IdentificationIEEE Transactions on Automatic Control, volume 56, issue 12, pages 2825-2840, December 2011.

Thesis

Refereed Conference Proceedings

  • L. Dodero, Hà Quang Minh, M. San Biagio, V. Murino and D. Sona. Kernel-based Classification For Brain Connectivity Graphs On The Riemannian Manifold Of Positive Definite MatricesProceedings of the International Symposium on Biomedical Imaging (ISBI 2015), New York, USA, April 2015, available online at http://ieeexplore.ieee.org/document/7163812/
  • V.Sindhwani, H.Q. Minh and A.C. Lozano. Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger CausalityProceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI 2013), July 2013, Bellevue, Washington, USA (Microsoft Best Paper Award), available online at http://www.auai.org/uai2013/prints/papers/25.pdf
  • Hà Quang Minh, Loris Bazzani, and Vittorio Murino. A unifying framework for vector-valued manifold regularization and multi-view learning. Proceedings of the 30th International Conference on Machine Learning (ICML 2013), June 2013, Atlanta, Georgia, USA.
  • D. Figueira, L. Bazzani, H.Q. Minh, M. Cristani, A. Bernardino, and V. Murino. Semi-supervised multi-feature learning for person re-identification. Proceedings of the 10th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS 2013), Krakow, Poland, August 2013.
  • G. Roffo, M. Cristani, L. Bazzani, H.Q. Minh, V. Murino. Trusting Skype: Learning the Way People Chat for Fast User Recognition and Verification. IEEE Workshop in Decoding Subtle Cues from Social Interactions, in conjunction with ICCV 2013, Sydney, Australia, December 2013.
  • Hà Quang Minh, Marco Cristani, Alessandro Perina and Vittorio Murino. A regularized spectral algorithm for Hidden Markov Models with applications in computer visionProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), June 2012, Providence, RI, USA. 
  • Hà Quang Minh and Laurenz Wiskott. Slow feature analysis and decorrelation filtering for separating correlated sources. Proceedings of the 13th IEEE International Conference on Computer Vision (ICCV 2011), November 2011, Barcelona, Spain. 
  • Hà Quang Minh and Vikas Sindhwani. Vector-valued Manifold RegularizationProceedings of the 28th International Conference on Machine Learning (ICML 2011), June 2011, Bellevue, Washinton, USA.
  • Minh Ha Quang, Gianluigi Pillonetto and Alessandro Chiuso. Nonlinear system identification via Gaussian regression and mixtures of kernels, Proceedings of the 15th IFAC Symposium on System Identification (SYSID 2009), Saint-Malo, France, July 2009.
  • Minh Ha Quang, Sung Ha Kang, and Triet Le. Reproducing kernels and colorization, Proceedings of the 8th International Conference on Sampling Theory and Applications (SAMPTA 09), Luminy, France, May 2009.

 Book chapter

  •  Hà Quang Minh and Vittorio Murino. From Covariance Matrices to Covariance Operators: Data Representation from Finite to Infinite-Dimensional Settings. In Algorithmic Advances in Riemannian Geometry and Applications: For Machine Learning, Computer Vision, Statistics, and Optimization. Springer series in Advances in Computer Vision and Pattern Recognition, 2016, available online at http://link.springer.com/chapter/10.1007/978-3-319-45026-1_5

Preprints

 

Awards

UAI 2013 Microsoft Best Paper Award.

IBM Pat Goldberg Memorial Best Paper Award, 2013.