102 citations · 528 across the 28 of their papers we have counts for
28 papers
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…
Handling Hard Affine SDP Shape Constraints in RKHSs
Pierre-Cyril Aubin-Frankowski, Zoltan Szabo
Shape constraints, such as non-negativity, monotonicity, convexity or supermodularity, play a key role in various applications of machine learning and statistics. However, incorpor…
Hard Shape-Constrained Kernel Machines
Pierre-Cyril Aubin-Frankowski, Zoltan Szabo
Shape constraints (such as non-negativity, monotonicity, convexity) play a central role in a large number of applications, as they usually improve performance for small sample size…
On Kernel Derivative Approximation with Random Fourier Features
Zoltan Szabo, Bharath K. Sriperumbudur
Random Fourier features (RFF) represent one of the most popular and wide-spread techniques in machine learning to scale up kernel algorithms. Despite the numerous successful applic…
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…
MONK -- Outlier-Robust Mean Embedding Estimation by Median-of-Means
Matthieu Lerasle, Zoltan Szabo, Timothee Mathieu +1
Mean embeddings provide an extremely flexible and powerful tool in machine learning and statistics to represent probability distributions and define a semi-metric (MMD, maximum mea…