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Found 5 papers, 3 papers with code
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms
The goal of this paper is to revisit Kernel Principal Component Analysis (KPCA) through dualization of a difference of convex functions.
Functional Output Regression with Infimal Convolution: Exploring the Huber and $ε$-insensitive Losses
The efficiency of the approach is demonstrated and contrasted with the classical squared loss setting on both synthetic and real-world benchmarks.
Emotion Transfer Using Vector-Valued Infinite Task Learning
Style transfer is a significant problem of machine learning with numerous successful applications.
Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses
Operator-Valued Kernels (OVKs) and associated vector-valued Reproducing Kernel Hilbert Spaces provide an elegant way to extend scalar kernel methods when the output space is a Hilbert space.
Infinite-Task Learning with RKHSs
A step further consists of learning a continuum of tasks for various loss functions.
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