Minh Ha Quang
+39 010 71781 936
- PhD, Mathematics, Brown University, USA, May 2006 (advisor: Steve Smale).
- BSc, Mathematics and Computer Science, Monash University, Melbourne, Australia.
Previous Academic Affiliation
- Humboldt University of Berlin, Postdoctoral Research Associate, October 1st 2008 - Septemer 30 2010.
- University of Vienna, Postdoctoral Research Associate, Department of Statistics, October 2006 - August 2007 and April 2008 - September 2008.
- Erwin Schrodinger International Institute for Mathematical Physics, Vienna, Junior Research Fellow, January - March 2008.
- University of California, Los Angeles, Fellow, Institute for Pure and Applied Mathematics, September - December 2007.
- University of Chicago, Research Associate, Department of Computer Science, September 2004 - September 2006.
- Hà Quang Minh and Vittorio Murino. Covariances in Computer Vision and Machine Learning. Morgan & Claypool Synthesis Lectures on Computer Vision, 2017
- 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 .
- Hà Quang Minh. Infinite-dimensional Log-Determinant divergences between positive definite trace class operators. Linear Algebra and Its Applications, 528 (2017) 331-383, available online since September 16 2016 at http://www.sciencedirect.com/science/article/pii/S0024379516304177 (53 pages)
- D. Felice, Minh Hà Quang, and S. Mancini.The volume of Gaussian states by information geometry. Journal of Mathematical Physics, 58, 012201 (2017), available online at http://aip.scitation.org/doi/abs/10.1063/1.4973507 (preprint at https://arxiv.org/abs/1509.01049)
- 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 Learning. Journal 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 separation. IEEE 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 Identification, IEEE Transactions on Automatic Control, volume 56, issue 12, pages 2825-2840, December 2011.
- Ha Quang Minh. The regularized least squares algorithm and the problem of learning halfspaces, Information Processing Letters, volume 111, issue 8, March 2011, pages 395-401.
- Minh Ha Quang, Sung Ha Kang, and Triet Le, Image and video colorization using vector-valued reproducing kernel Hilbert spaces, Journal of Mathematical Imaging and Vision, volume 37, number 1, pages 49-65, 2010.
- Ha Quang Minh. Some Properties of Gaussian Reproducing Kernel Hilbert Spaces and Their Implications for Function Approximation and Learning Theory, Constructive Approximation, volume 32, number 2, pages 307-338, 2010.
- Reproducing Kernel Hilbert Spaces in Learning Theory , PhD Thesis in Mathematics, Brown University, May 2006 (advisor: Steve Smale)
Refereed Conference Proceedings
- Hà Quang Minh. Log-Determinant divergences between positive definite Hilbert-Schmidt operators. Geometric Science of Information (GSI 2017), Paris, France, November 2017, extended arxiv version available online at https://arxiv.org/abs/1702.03425
- Hà Quang Minh, Marco San Biagio, Loris Bazzani, and Vittorio Murino. Approximate Log-Hilbert-Schmidt distances between covariance operators for image classification. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), Las Vegas, Nevada, USA, June 2016, available online at http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Minh_Approximate_Log-Hilbert-Schmidt_Distances_CVPR_2016_paper.html
- Hà Quang Minh. Affine-invariant Riemannian Distance Between Infinite-dimensional Covariance Operators. Geometric Science of Information (GSI 2015), Paris-Saclay, France, October 2015, available online at http://link.springer.com/chapter/10.1007%2F978-3-319-25040-3_4
- 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 Matrices. Proceedings of the International Symposium on Biomedical Imaging (ISBI 2015), New York, USA, April 2015, available online at http://ieeexplore.ieee.org/document/7163812/
- Hà Quang Minh, Marco San Biagio, and Vittorio Murino. Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces. Advances in Neural Information Processing Systems (NIPS 2014), December 2014, Montreal, Canada, available online at https://papers.nips.cc/paper/5457-log-hilbert-schmidt-metric-between-positive-definite-operators-on-hilbert-spaces
- V.Sindhwani, H.Q. Minh and A.C. Lozano. Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality. Proceedings 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 vision. Proceedings 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 Regularization. Proceedings 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.
- Ha Quang Minh, Partha Niyogi and Yuan Yao. Mercer's Theorem, Feature Maps, and Smoothing, Proceedings of the 19th Annual Conference on Learning Theory (COLT 2006), Springer Lecture Notes in Computer Science volume 4005, pages 154-168.
- Ha Quang Minh and Thomas Hofmann. Learning over Compact Metric Spaces, Proceedings of the 17th Annual Conference on Learning Theory (COLT 2004),Springer Lecture Notes in Artificial Intelligence volume 3120, pages 239-254.
- 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
- Error Estimates in Learning Theory with Effective Dimensionality, preprint.
- Reproducing Kernel Hilbert Spaces and Learning Problems on the Hypercube , preprint.
- Reproducing Kernel Hilbert Spaces in Learning Theory: the Sphere and the Hypercube , preprint.
UAI 2013 Microsoft Best Paper Award.
IBM Pat Goldberg Memorial Best Paper Award, 2013.