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Papers
Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection
JMLR 2025
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
AISTATS 2024
Practical Markov Boundary Learning without Strong Assumptions
AAAI 2023
On Mahalanobis Distance in Functional Settings
JMLR 2020
Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification
CVPR 2016
Covariance-based Clustering in Multivariate and Functional Data Analysis
JMLR 2016
Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces
NeurIPS 2014
A Generalized Kernel Approach to Structured Output Learning
ICML 2013
On the Sample Complexity of Subspace Learning
NeurIPS 2013
Statistical Consistency of Kernel Canonical Correlation Analysis
JMLR 2007
Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces
JMLR 2004