Learning Over Compact Metric Spaces. - Ha Quang Minh; Thomas Hofmann 001 (COLT, 2004)

Mercer's Theorem, Feature Maps, and Smoothing. - Ha Quang Minh; Partha Niyogi; Yuan Yao (COLT, 2006)

Image and Video Colorization Using Vector-Valued Reproducing Kernel Hilbert Spaces. - Minh Ha Quang; Sung Ha Kang; Triet M. Le (J. Math. Imaging Vis., 2010)

Slow feature analysis and decorrelation filtering for separating correlated sources. - Ha Quang Minh; Laurenz Wiskott (ICCV, 2011)

Vector-valued Manifold Regularization. - Ha Quang Minh; Vikas Sindhwani (ICML, 2011)

The regularized least squares algorithm and the problem of learning halfspaces. - Ha Quang Minh (Inf. Process. Lett., 2011)

A New Kernel-Based Approach for NonlinearSystem Identification. - Gianluigi Pillonetto; Minh Ha Quang; Alessandro Chiuso (IEEE Trans. Autom. Control., 2011)

A regularized spectral algorithm for Hidden Markov Models with applications in computer vision. - Ha Quang Minh; Marco Cristani; Alessandro Perina; Vittorio Murino (CVPR, 2012)

Scalable Matrix-valued Kernel Learning and High-dimensional Nonlinear Causal Inference - Vikas Sindhwani; Aurélie C. Lozano; Ha Quang Minh (CoRR, 2012)

Semi-supervised multi-feature learning for person re-identification. - Dario Figueira; Loris Bazzani; Ha Quang Minh; Marco Cristani; Alexandre Bernardino; Vittorio Murino (AVSS, 2013)

Trusting Skype: Learning the Way People Chat for Fast User Recognition and Verification. - Giorgio Roffo; Marco Cristani; Loris Bazzani; Ha Quang Minh; Vittorio Murino (ICCV Workshops, 2013)

A unifying framework for vector-valued manifold regularization and multi-view learning. - Ha Quang Minh; Loris Bazzani; Vittorio Murino (ICML (2), 2013)

Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality. - Vikas Sindhwani; Ha Quang Minh; Aurélie C. Lozano (UAI, 2013)

Multivariate Slow Feature Analysis and Decorrelation Filtering for Blind Source Separation. - Ha Quang Minh; Laurenz Wiskott (IEEE Trans. Image Process., 2013)

Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces. - Minh Ha Quang; Marco San-Biagio; Vittorio Murino (NIPS, 2014)

Affine-Invariant Riemannian Distance Between Infinite-Dimensional Covariance Operators. - Ha Quang Minh (GSI, 2015)

Kernel-based classification for brain connectivity graphs on the Riemannian manifold of positive definite matrices. - Luca Dodero; Ha Quang Minh; Marco San-Biagio; Vittorio Murino; Diego Sona (ISBI, 2015)

Approximate Log-Hilbert-Schmidt Distances between Covariance Operators for Image Classification. - Ha Quang Minh; Marco San-Biagio; Loris Bazzani; Vittorio Murino (CVPR, 2016)

Operator-Valued Bochner Theorem, Fourier Feature Maps for Operator-Valued Kernels, and Vector-Valued Learning. - Ha Quang Minh (CoRR, 2016)

Kernel Methods on Approximate Infinite-Dimensional Covariance Operators for Image Classification. - Ha Quang Minh; Marco San-Biagio; Loris Bazzani; Vittorio Murino (CoRR, 2016)

Infinite-dimensional Log-Determinant divergences II: Alpha-Beta divergences. - Minh Ha Quang (CoRR, 2016)

A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning. - Ha Quang Minh; Loris Bazzani; Vittorio Murino (J. Mach. Learn. Res., 2016)

Log-Determinant Divergences Between Positive Definite Hilbert-Schmidt Operators. - Ha Quang Minh (GSI, 2017)

Covariances in Computer Vision and Machine Learning - Ha Quang Minh; Vittorio Murino (Morgan & Claypool Publishers, 2017)

Approximate Log-Determinant Divergences Between Covariance Operators and Applications. - Hà Quang Minh (ACSSC, 2019)

A Unified Formulation for the Bures-Wasserstein and Log-Euclidean/Log-Hilbert-Schmidt Distances between Positive Definite Operators. - Hà Quang Minh (GSI, 2019)

Entropy-Regularized 2-Wasserstein Distance between Gaussian Measures. - Anton Mallasto; Augusto Gerolin; Hà Quang Minh (CoRR, 2020)

Entropic regularization of Wasserstein distance between infinite-dimensional Gaussian measures and Gaussian processes. - Minh Ha Quang (CoRR, 2020)

Quantum Jensen-Shannon Divergences Between Infinite-Dimensional Positive Definite Operators. - Hà Quang Minh (GSI, 2021)

Convergence and finite sample approximations of entropic regularized Wasserstein distances in Gaussian and RKHS settings. - Minh Ha Quang (CoRR, 2021)

Estimation of Riemannian distances between covariance operators and Gaussian processes. - Ha Quang Minh (CoRR, 2021)

Kullback-Leibler and Renyi divergences in reproducing kernel Hilbert space and Gaussian process settings. - Minh Ha Quang (CoRR, 2022)

Finite Sample Approximations of Exact and Entropic Wasserstein Distances Between Covariance Operators and Gaussian Processes. - Hà Quang Minh (SIAM/ASA J. Uncertain. Quantification, 2022)

Fisher-Rao Riemannian Geometry of Equivalent Gaussian Measures on Hilbert Space. - Hà Quang Minh (GSI (1), 2023)

Learning a Single Index Model from Anisotropic Data with Vanilla Stochastic Gradient Descent. - Guillaume Braun; Minh Ha Quang; Masaaki Imaizumi (AISTATS, 2025)

Categorical and geometric methods in statistical, manifold, and machine learning. - Hông Vân Lê; Hà Quang Minh; Frédéric Protin; Wilderich Tuschmann (CoRR, 2025)

Geometric Jensen-Shannon Divergence Between Gaussian Measures On Hilbert Space. - Minh Ha Quang; Frank Nielsen (CoRR, 2025)