1 citations · 3 across the 5 of their papers we have counts for
5 papers
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms
Francesco Tonin, Alex Lambert, Panagiotis Patrinos +1
The goal of this paper is to revisit Kernel Principal Component Analysis (KPCA) through dualization of a difference of convex functions. This allows to naturally extend KPCA to mul…
Functional Output Regression with Infimal Convolution: Exploring the Huber and -insensitive Losses
Alex Lambert, Dimitri Bouche, Zoltan Szabo +1
The focus of the paper is functional output regression (FOR) with convoluted losses. While most existing work consider the square loss setting, we leverage extensions of the Huber…
Emotion Transfer Using Vector-Valued Infinite Task Learning
Alex Lambert, Sanjeel Parekh, Zoltán Szabó +1
Style transfer is a significant problem of machine learning with numerous successful applications. In this work, we present a novel style transfer framework building upon infinite…
Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses
Pierre Laforgue, Alex Lambert, Luc Brogat-Motte +1
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 Hilb…
Infinite-Task Learning with RKHSs
Romain Brault, Alex Lambert, Zoltán Szabó +2
Machine learning has witnessed tremendous success in solving tasks depending on a single hyperparameter. When considering simultaneously a finite number of tasks, multi-task learni…