Multi-task Multi-Kernel Learning for Estimating Individual Wellbeing

Abstract

We apply a recently proposed technique – Multi-task Multi-Kernel Learning (MTMKL) – to the problem of modeling students’ wellbeing. Because wellbeing is a complex internal state consisting of several related dimensions, Multi-task learning can be used to classify them simultaneously. Multiple Kernel Learning is used to efficiently combine data from multiple modalities. MTMKL combines these approaches using an optimization function similar to a support vector machine (SVM). We show that MTMKL successfully classifies five dimensions of wellbeing, and provides performance benefits above both SVM and MKL.

Publication
In Neural Information Processing Systems (NeurIPS) Workshop on Multimodal Machine Learning
Natasha Jaques
Natasha Jaques

My research is focused on Social Reinforcement Learning–developing algorithms that use insights from social learning to improve AI agents' learning, generalization, coordination, and human-AI interaction.

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