2 citations · 3 across the 3 of their papers we have counts for
3 papers
Deep Sketched Output Kernel Regression for Structured Prediction
Tamim El Ahmad, Junjie Yang, Pierre Laforgue +1
By leveraging the kernel trick in the output space, kernel-induced losses provide a principled way to define structured output prediction tasks for a wide variety of output modalit…
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
Tamim El Ahmad, Luc Brogat-Motte, Pierre Laforgue +1
Leveraging the kernel trick in both the input and output spaces, surrogate kernel methods are a flexible and theoretically grounded solution to structured output prediction. If the…
Fast Kernel Methods for Generic Lipschitz Losses via -Sparsified Sketches
Tamim El Ahmad, Pierre Laforgue, Florence d'Alché-Buc
Kernel methods are learning algorithms that enjoy solid theoretical foundations while suffering from important computational limitations. Sketching, which consists in looking for s…