11 citations · 27 across the 8 of their papers we have counts for
8 papers
Kullback-Leibler and Renyi divergences in reproducing kernel Hilbert space and Gaussian process settings
Minh Ha Quang
In this work, we present formulations for regularized Kullback-Leibler and Rényi divergences via the Alpha Log-Determinant (Log-Det) divergences between positive Hilbert-Schmidt op…
Estimation of Riemannian distances between covariance operators and Gaussian processes
Ha Quang Minh
In this work we study two Riemannian distances between infinite-dimensional positive definite Hilbert-Schmidt operators, namely affine-invariant Riemannian and Log-Hilbert-Schmidt…
Infinite-dimensional Log-Determinant divergences between positive definite Hilbert-Schmidt operators
Minh Ha Quang
The current work generalizes the author's previous work on the infinite-dimensional Alpha Log-Determinant (Log-Det) divergences and Alpha-Beta Log-Det divergences, defined on the s…
Kernel Methods on Approximate Infinite-Dimensional Covariance Operators for Image Classification
Hà Quang Minh, Marco San Biagio, Loris Bazzani +1
This paper presents a novel framework for visual object recognition using infinite-dimensional covariance operators of input features in the paradigm of kernel methods on infinite-…
Operator-Valued Bochner Theorem, Fourier Feature Maps for Operator-Valued Kernels, and Vector-Valued Learning
Ha Quang Minh
This paper presents a framework for computing random operator-valued feature maps for operator-valued positive definite kernels. This is a generalization of the random Fourier feat…
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality
Vikas Sindhwani, Ha Quang Minh, Aurelie Lozano
We propose a general matrix-valued multiple kernel learning framework for high-dimensional nonlinear multivariate regression problems. This framework allows a broad class of mixed…
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
This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) framework for the problem of learning an unknown functional dependency between a structured inp…
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality
Vikas Sindhwani, Minh Ha Quang, Aurelie C. Lozano
We propose a general matrix-valued multiple kernel learning framework for high-dimensional nonlinear multivariate regression problems. This framework allows a broad class of mixed…