Related papers: Meta-learning Amidst Heterogeneity and Ambiguity

Meta-learning Amidst Heterogeneity and Ambiguity

URL: http://arxiv.org/abs/2107.02228v1
Date: Mon, 5 Jul 2021 18:54:31 GMT
Title: Meta-learning Amidst Heterogeneity and Ambiguity
Authors: Kyeongryeol Go, Seyoung Yun
Abstract summary: We devise a novel meta-learning framework, called Meta-learning Amidst Heterogeneity and Ambiguity (MAHA) By extensively conducting several experiments in regression and classification, we demonstrate the validity of our model.
Score: 11.061517140668961
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Meta-learning aims to learn a model that can handle multiple tasks generated from an unknown but shared distribution. However, typical meta-learning algorithms have assumed the tasks to be similar such that a single meta-learner is sufficient to aggregate the variations in all aspects. In addition, there has been less consideration on uncertainty when limited information is given as context. In this paper, we devise a novel meta-learning framework, called Meta-learning Amidst Heterogeneity and Ambiguity (MAHA), that outperforms previous works in terms of prediction based on its ability on task identification. By extensively conducting several experiments in regression and classification, we demonstrate the validity of our model, which turns out to be robust to both task heterogeneity and ambiguity.

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