@report {1616530,
	title = {Meta-learning for mixed linear regression},
	year = {2020},
	institution = {ArXiv Report},
	address = {ICML},
	abstract = {In modern supervised learning, there are a large number of tasks, but many of them are associated with only a small amount of labeled data. These include data from medical image processing and robotic interaction. Even though each individual task cannot be meaningfully trained in isolation, one seeks to meta-learn across the tasks from past experiences by exploiting some similarities. We study a fundamental question of interest: When can abundant tasks with small data compensate for lack of tasks with big data? We focus on a canonical scenario where each task is drawn from a mixture of k linear regressions, and identify sufficient conditions for such a graceful exchange to hold; The total number of examples necessary with only small data tasks scales similarly as when big data tasks are available. To this end, we introduce a novel spectral approach and show that we can efficiently utilize small data tasks with the help of Ω̃ (k3/2) medium data tasks each with Ω̃ (k1/2) examples.},
	url = {https://arxiv.org/abs/2002.08936},
	author = {Weihao Kong and Raghav Somani and Zhao Song and Sham Kakade and Sewoong Oh}
}
