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Found 5 papers, 1 papers with code
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
Surrogate kernel-based methods offer a flexible solution to structured output prediction by leveraging the kernel trick in both input and output spaces.
Vector-Valued Least-Squares Regression under Output Regularity Assumptions
We propose and analyse a reduced-rank method for solving least-squares regression problems with infinite dimensional output.
Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters
This paper introduces a novel and generic framework to solve the flagship task of supervised labeled graph prediction by leveraging Optimal Transport tools.
Learning Output Embeddings in Structured Prediction
A powerful and flexible approach to structured prediction consists in embedding the structured objects to be predicted into a feature space of possibly infinite dimension by means of output kernels, and then, solving a regression problem in this output space.
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.
